fix: ModelTrainer and HyperparameterTuner missing environment variables (5613)

sagemaker-train/src/sagemaker/train/tuner.py

@@ -1504,7 +1504,13 @@ def _build_training_job_definition(self, inputs):
  model_trainer.stopping_condition.max_wait_time_in_seconds
  )
 
- definition = HyperParameterTrainingJobDefinition(
+ # Propagate environment variables from ModelTrainer.
+ # Only include when it's a dict (even empty); omit otherwise so the
+ # Pydantic field stays Unassigned and is excluded during serialization.
+ env = model_trainer.environment
+
+ # Build base kwargs for the definition
+ definition_kwargs = dict(
  algorithm_specification=algorithm_spec,
  role_arn=model_trainer.role,
  input_data_config=input_data_config if input_data_config else None,
@@ -1515,10 +1521,11 @@ def _build_training_job_definition(self, inputs):
  enable_managed_spot_training=model_trainer.compute.enable_managed_spot_training,
  )
 
- # Pass through environment variables from model_trainer
- env = getattr(model_trainer, "environment", None)
- if env and isinstance(env, dict):
- definition.environment = env
+ # Include environment only when it's a dict (including empty).
+ if isinstance(env, dict):
+ definition_kwargs["environment"] = env
+
+ definition = HyperParameterTrainingJobDefinition(**definition_kwargs)
 
  # Pass through VPC config from model_trainer
  networking = getattr(model_trainer, "networking", None)

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

@@ -596,3 +596,73 @@ def test_build_training_job_definition_includes_spot_params(self):
  assert isinstance(
  definition.stopping_condition.max_wait_time_in_seconds, int
  ), "Max wait time should be set"
+
+ def test_build_training_job_definition_includes_environment_variables(self):
+ """Test that _build_training_job_definition includes environment variables.
+
+ This test verifies the fix for GitHub issue #5613 where tuning jobs were
+ missing environment variables that were set on the ModelTrainer.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment is not None, "Environment should not be None"
+ assert definition.environment == {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }, "Environment variables should match those set on ModelTrainer"
+
+ def test_build_training_job_definition_with_none_environment(self):
+ """Test that _build_training_job_definition handles None environment gracefully.
+
+ When environment is None, it should not be passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
+ from sagemaker.core.utils.utils import Unassigned
+
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = None
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert isinstance(definition.environment, Unassigned), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
+
+ def test_build_training_job_definition_with_empty_environment(self):
+ """Test that _build_training_job_definition passes through empty environment.
+
+ An empty dict is valid for the SageMaker API, so we pass it through as-is
+ rather than silently converting it to None.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {}
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )

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

@@ -405,8 +405,31 @@ def test_passes_environment_variables(self):
  definition = tuner._build_training_job_definition(inputs=None)
  assert definition.environment == {"MY_VAR": "value", "OTHER": "123"}
 
+ def test_passes_empty_environment(self):
+ """Should pass through empty dict environment as-is.
+
+ An empty dict is valid for the SageMaker API, so we pass it through
+ rather than silently converting it to None/Unassigned.
+ """
+ trainer = _mock_model_trainer(environment={})
+
+ tuner = HyperparameterTuner(
+ model_trainer=trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_hp_ranges(),
+ )
+
+ definition = tuner._build_training_job_definition(inputs=None)
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )
+
  def test_skips_environment_when_none(self):
- """Should not set environment when model_trainer.environment is None."""
+ """Should not set environment when model_trainer.environment is None.
+
+ When environment is None, it is not passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
  trainer = _mock_model_trainer(environment=None)
 
  tuner = HyperparameterTuner(
@@ -416,10 +439,16 @@ def test_skips_environment_when_none(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
 
  def test_skips_environment_when_not_dict(self):
- """Should not set environment when it's not a dict (e.g. MagicMock)."""
+ """Should not set environment when it's not a dict (e.g. MagicMock).
+
+ Non-dict values are not passed to the Pydantic constructor to avoid
+ validation errors. The field stays as Unassigned.
+ """
  trainer = _mock_model_trainer(environment=MagicMock())
 
  tuner = HyperparameterTuner(
@@ -429,7 +458,9 @@ def test_skips_environment_when_not_dict(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is not a dict"
+ )
 
  def test_passes_vpc_config(self):
  """Should set definition.vpc_config from model_trainer.networking._to_vpc_config()."""

sagemaker-train/src/sagemaker/train/tuner.py

@@ -1504,7 +1504,13 @@ def _build_training_job_definition(self, inputs):
  model_trainer.stopping_condition.max_wait_time_in_seconds
  )
 
- definition = HyperParameterTrainingJobDefinition(
+ # Propagate environment variables from ModelTrainer.
+ # Only include when it's a dict (even empty); omit otherwise so the
+ # Pydantic field stays Unassigned and is excluded during serialization.
+ env = model_trainer.environment
+
+ # Build base kwargs for the definition
+ definition_kwargs = dict(
  algorithm_specification=algorithm_spec,
  role_arn=model_trainer.role,
  input_data_config=input_data_config if input_data_config else None,
@@ -1515,10 +1521,11 @@ def _build_training_job_definition(self, inputs):
  enable_managed_spot_training=model_trainer.compute.enable_managed_spot_training,
  )
 
- # Pass through environment variables from model_trainer
- env = getattr(model_trainer, "environment", None)
- if env and isinstance(env, dict):
- definition.environment = env
+ # Include environment only when it's a dict (including empty).
+ if isinstance(env, dict):
+ definition_kwargs["environment"] = env
+
+ definition = HyperParameterTrainingJobDefinition(**definition_kwargs)
 
  # Pass through VPC config from model_trainer
  networking = getattr(model_trainer, "networking", None)

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

@@ -596,3 +596,73 @@ def test_build_training_job_definition_includes_spot_params(self):
  assert isinstance(
  definition.stopping_condition.max_wait_time_in_seconds, int
  ), "Max wait time should be set"
+
+ def test_build_training_job_definition_includes_environment_variables(self):
+ """Test that _build_training_job_definition includes environment variables.
+
+ This test verifies the fix for GitHub issue #5613 where tuning jobs were
+ missing environment variables that were set on the ModelTrainer.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment is not None, "Environment should not be None"
+ assert definition.environment == {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }, "Environment variables should match those set on ModelTrainer"
+
+ def test_build_training_job_definition_with_none_environment(self):
+ """Test that _build_training_job_definition handles None environment gracefully.
+
+ When environment is None, it should not be passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
+ from sagemaker.core.utils.utils import Unassigned
+
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = None
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert isinstance(definition.environment, Unassigned), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
+
+ def test_build_training_job_definition_with_empty_environment(self):
+ """Test that _build_training_job_definition passes through empty environment.
+
+ An empty dict is valid for the SageMaker API, so we pass it through as-is
+ rather than silently converting it to None.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {}
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )

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

@@ -405,8 +405,31 @@ def test_passes_environment_variables(self):
  definition = tuner._build_training_job_definition(inputs=None)
  assert definition.environment == {"MY_VAR": "value", "OTHER": "123"}
 
+ def test_passes_empty_environment(self):
+ """Should pass through empty dict environment as-is.
+
+ An empty dict is valid for the SageMaker API, so we pass it through
+ rather than silently converting it to None/Unassigned.
+ """
+ trainer = _mock_model_trainer(environment={})
+
+ tuner = HyperparameterTuner(
+ model_trainer=trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_hp_ranges(),
+ )
+
+ definition = tuner._build_training_job_definition(inputs=None)
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )
+
  def test_skips_environment_when_none(self):
- """Should not set environment when model_trainer.environment is None."""
+ """Should not set environment when model_trainer.environment is None.
+
+ When environment is None, it is not passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
  trainer = _mock_model_trainer(environment=None)
 
  tuner = HyperparameterTuner(
@@ -416,10 +439,16 @@ def test_skips_environment_when_none(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
 
  def test_skips_environment_when_not_dict(self):
- """Should not set environment when it's not a dict (e.g. MagicMock)."""
+ """Should not set environment when it's not a dict (e.g. MagicMock).
+
+ Non-dict values are not passed to the Pydantic constructor to avoid
+ validation errors. The field stays as Unassigned.
+ """
  trainer = _mock_model_trainer(environment=MagicMock())
 
  tuner = HyperparameterTuner(
@@ -429,7 +458,9 @@ def test_skips_environment_when_not_dict(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is not a dict"
+ )
 
  def test_passes_vpc_config(self):
  """Should set definition.vpc_config from model_trainer.networking._to_vpc_config()."""

sagemaker-train/src/sagemaker/train/tuner.py

@@ -1504,7 +1504,13 @@ def _build_training_job_definition(self, inputs):
  model_trainer.stopping_condition.max_wait_time_in_seconds
  )
 
- definition = HyperParameterTrainingJobDefinition(
+ # Propagate environment variables from ModelTrainer.
+ # Only include when it's a dict (even empty); omit otherwise so the
+ # Pydantic field stays Unassigned and is excluded during serialization.
+ env = model_trainer.environment
+
+ # Build base kwargs for the definition
+ definition_kwargs = dict(
  algorithm_specification=algorithm_spec,
  role_arn=model_trainer.role,
  input_data_config=input_data_config if input_data_config else None,
@@ -1515,10 +1521,11 @@ def _build_training_job_definition(self, inputs):
  enable_managed_spot_training=model_trainer.compute.enable_managed_spot_training,
  )
 
- # Pass through environment variables from model_trainer
- env = getattr(model_trainer, "environment", None)
- if env and isinstance(env, dict):
- definition.environment = env
+ # Include environment only when it's a dict (including empty).
+ if isinstance(env, dict):
+ definition_kwargs["environment"] = env
+
+ definition = HyperParameterTrainingJobDefinition(**definition_kwargs)
 
  # Pass through VPC config from model_trainer
  networking = getattr(model_trainer, "networking", None)

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

@@ -596,3 +596,73 @@ def test_build_training_job_definition_includes_spot_params(self):
  assert isinstance(
  definition.stopping_condition.max_wait_time_in_seconds, int
  ), "Max wait time should be set"
+
+ def test_build_training_job_definition_includes_environment_variables(self):
+ """Test that _build_training_job_definition includes environment variables.
+
+ This test verifies the fix for GitHub issue #5613 where tuning jobs were
+ missing environment variables that were set on the ModelTrainer.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment is not None, "Environment should not be None"
+ assert definition.environment == {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }, "Environment variables should match those set on ModelTrainer"
+
+ def test_build_training_job_definition_with_none_environment(self):
+ """Test that _build_training_job_definition handles None environment gracefully.
+
+ When environment is None, it should not be passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
+ from sagemaker.core.utils.utils import Unassigned
+
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = None
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert isinstance(definition.environment, Unassigned), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
+
+ def test_build_training_job_definition_with_empty_environment(self):
+ """Test that _build_training_job_definition passes through empty environment.
+
+ An empty dict is valid for the SageMaker API, so we pass it through as-is
+ rather than silently converting it to None.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {}
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )

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

@@ -405,8 +405,31 @@ def test_passes_environment_variables(self):
  definition = tuner._build_training_job_definition(inputs=None)
  assert definition.environment == {"MY_VAR": "value", "OTHER": "123"}
 
+ def test_passes_empty_environment(self):
+ """Should pass through empty dict environment as-is.
+
+ An empty dict is valid for the SageMaker API, so we pass it through
+ rather than silently converting it to None/Unassigned.
+ """
+ trainer = _mock_model_trainer(environment={})
+
+ tuner = HyperparameterTuner(
+ model_trainer=trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_hp_ranges(),
+ )
+
+ definition = tuner._build_training_job_definition(inputs=None)
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )
+
  def test_skips_environment_when_none(self):
- """Should not set environment when model_trainer.environment is None."""
+ """Should not set environment when model_trainer.environment is None.
+
+ When environment is None, it is not passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
  trainer = _mock_model_trainer(environment=None)
 
  tuner = HyperparameterTuner(
@@ -416,10 +439,16 @@ def test_skips_environment_when_none(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
 
  def test_skips_environment_when_not_dict(self):
- """Should not set environment when it's not a dict (e.g. MagicMock)."""
+ """Should not set environment when it's not a dict (e.g. MagicMock).
+
+ Non-dict values are not passed to the Pydantic constructor to avoid
+ validation errors. The field stays as Unassigned.
+ """
  trainer = _mock_model_trainer(environment=MagicMock())
 
  tuner = HyperparameterTuner(
@@ -429,7 +458,9 @@ def test_skips_environment_when_not_dict(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is not a dict"
+ )
 
  def test_passes_vpc_config(self):
  """Should set definition.vpc_config from model_trainer.networking._to_vpc_config()."""

sagemaker-train/src/sagemaker/train/tuner.py

@@ -1504,7 +1504,13 @@ def _build_training_job_definition(self, inputs):
  model_trainer.stopping_condition.max_wait_time_in_seconds
  )
 
- definition = HyperParameterTrainingJobDefinition(
+ # Propagate environment variables from ModelTrainer.
+ # Only include when it's a dict (even empty); omit otherwise so the
+ # Pydantic field stays Unassigned and is excluded during serialization.
+ env = model_trainer.environment
+
+ # Build base kwargs for the definition
+ definition_kwargs = dict(
  algorithm_specification=algorithm_spec,
  role_arn=model_trainer.role,
  input_data_config=input_data_config if input_data_config else None,
@@ -1515,10 +1521,11 @@ def _build_training_job_definition(self, inputs):
  enable_managed_spot_training=model_trainer.compute.enable_managed_spot_training,
  )
 
- # Pass through environment variables from model_trainer
- env = getattr(model_trainer, "environment", None)
- if env and isinstance(env, dict):
- definition.environment = env
+ # Include environment only when it's a dict (including empty).
+ if isinstance(env, dict):
+ definition_kwargs["environment"] = env
+
+ definition = HyperParameterTrainingJobDefinition(**definition_kwargs)
 
  # Pass through VPC config from model_trainer
  networking = getattr(model_trainer, "networking", None)

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

@@ -596,3 +596,73 @@ def test_build_training_job_definition_includes_spot_params(self):
  assert isinstance(
  definition.stopping_condition.max_wait_time_in_seconds, int
  ), "Max wait time should be set"
+
+ def test_build_training_job_definition_includes_environment_variables(self):
+ """Test that _build_training_job_definition includes environment variables.
+
+ This test verifies the fix for GitHub issue #5613 where tuning jobs were
+ missing environment variables that were set on the ModelTrainer.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment is not None, "Environment should not be None"
+ assert definition.environment == {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }, "Environment variables should match those set on ModelTrainer"
+
+ def test_build_training_job_definition_with_none_environment(self):
+ """Test that _build_training_job_definition handles None environment gracefully.
+
+ When environment is None, it should not be passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
+ from sagemaker.core.utils.utils import Unassigned
+
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = None
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert isinstance(definition.environment, Unassigned), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
+
+ def test_build_training_job_definition_with_empty_environment(self):
+ """Test that _build_training_job_definition passes through empty environment.
+
+ An empty dict is valid for the SageMaker API, so we pass it through as-is
+ rather than silently converting it to None.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {}
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )

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

@@ -405,8 +405,31 @@ def test_passes_environment_variables(self):
  definition = tuner._build_training_job_definition(inputs=None)
  assert definition.environment == {"MY_VAR": "value", "OTHER": "123"}
 
+ def test_passes_empty_environment(self):
+ """Should pass through empty dict environment as-is.
+
+ An empty dict is valid for the SageMaker API, so we pass it through
+ rather than silently converting it to None/Unassigned.
+ """
+ trainer = _mock_model_trainer(environment={})
+
+ tuner = HyperparameterTuner(
+ model_trainer=trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_hp_ranges(),
+ )
+
+ definition = tuner._build_training_job_definition(inputs=None)
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )
+
  def test_skips_environment_when_none(self):
- """Should not set environment when model_trainer.environment is None."""
+ """Should not set environment when model_trainer.environment is None.
+
+ When environment is None, it is not passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
  trainer = _mock_model_trainer(environment=None)
 
  tuner = HyperparameterTuner(
@@ -416,10 +439,16 @@ def test_skips_environment_when_none(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
 
  def test_skips_environment_when_not_dict(self):
- """Should not set environment when it's not a dict (e.g. MagicMock)."""
+ """Should not set environment when it's not a dict (e.g. MagicMock).
+
+ Non-dict values are not passed to the Pydantic constructor to avoid
+ validation errors. The field stays as Unassigned.
+ """
  trainer = _mock_model_trainer(environment=MagicMock())
 
  tuner = HyperparameterTuner(
@@ -429,7 +458,9 @@ def test_skips_environment_when_not_dict(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is not a dict"
+ )
 
  def test_passes_vpc_config(self):
  """Should set definition.vpc_config from model_trainer.networking._to_vpc_config()."""

sagemaker-train/src/sagemaker/train/tuner.py

@@ -1504,7 +1504,13 @@ def _build_training_job_definition(self, inputs):
  model_trainer.stopping_condition.max_wait_time_in_seconds
  )
 
- definition = HyperParameterTrainingJobDefinition(
+ # Propagate environment variables from ModelTrainer.
+ # Only include when it's a dict (even empty); omit otherwise so the
+ # Pydantic field stays Unassigned and is excluded during serialization.
+ env = model_trainer.environment
+
+ # Build base kwargs for the definition
+ definition_kwargs = dict(
  algorithm_specification=algorithm_spec,
  role_arn=model_trainer.role,
  input_data_config=input_data_config if input_data_config else None,
@@ -1515,10 +1521,11 @@ def _build_training_job_definition(self, inputs):
  enable_managed_spot_training=model_trainer.compute.enable_managed_spot_training,
  )
 
- # Pass through environment variables from model_trainer
- env = getattr(model_trainer, "environment", None)
- if env and isinstance(env, dict):
- definition.environment = env
+ # Include environment only when it's a dict (including empty).
+ if isinstance(env, dict):
+ definition_kwargs["environment"] = env
+
+ definition = HyperParameterTrainingJobDefinition(**definition_kwargs)
 
  # Pass through VPC config from model_trainer
  networking = getattr(model_trainer, "networking", None)

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

@@ -596,3 +596,73 @@ def test_build_training_job_definition_includes_spot_params(self):
  assert isinstance(
  definition.stopping_condition.max_wait_time_in_seconds, int
  ), "Max wait time should be set"
+
+ def test_build_training_job_definition_includes_environment_variables(self):
+ """Test that _build_training_job_definition includes environment variables.
+
+ This test verifies the fix for GitHub issue #5613 where tuning jobs were
+ missing environment variables that were set on the ModelTrainer.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment is not None, "Environment should not be None"
+ assert definition.environment == {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }, "Environment variables should match those set on ModelTrainer"
+
+ def test_build_training_job_definition_with_none_environment(self):
+ """Test that _build_training_job_definition handles None environment gracefully.
+
+ When environment is None, it should not be passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
+ from sagemaker.core.utils.utils import Unassigned
+
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = None
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert isinstance(definition.environment, Unassigned), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
+
+ def test_build_training_job_definition_with_empty_environment(self):
+ """Test that _build_training_job_definition passes through empty environment.
+
+ An empty dict is valid for the SageMaker API, so we pass it through as-is
+ rather than silently converting it to None.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {}
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )

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

@@ -405,8 +405,31 @@ def test_passes_environment_variables(self):
  definition = tuner._build_training_job_definition(inputs=None)
  assert definition.environment == {"MY_VAR": "value", "OTHER": "123"}
 
+ def test_passes_empty_environment(self):
+ """Should pass through empty dict environment as-is.
+
+ An empty dict is valid for the SageMaker API, so we pass it through
+ rather than silently converting it to None/Unassigned.
+ """
+ trainer = _mock_model_trainer(environment={})
+
+ tuner = HyperparameterTuner(
+ model_trainer=trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_hp_ranges(),
+ )
+
+ definition = tuner._build_training_job_definition(inputs=None)
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )
+
  def test_skips_environment_when_none(self):
- """Should not set environment when model_trainer.environment is None."""
+ """Should not set environment when model_trainer.environment is None.
+
+ When environment is None, it is not passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
  trainer = _mock_model_trainer(environment=None)
 
  tuner = HyperparameterTuner(
@@ -416,10 +439,16 @@ def test_skips_environment_when_none(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
 
  def test_skips_environment_when_not_dict(self):
- """Should not set environment when it's not a dict (e.g. MagicMock)."""
+ """Should not set environment when it's not a dict (e.g. MagicMock).
+
+ Non-dict values are not passed to the Pydantic constructor to avoid
+ validation errors. The field stays as Unassigned.
+ """
  trainer = _mock_model_trainer(environment=MagicMock())
 
  tuner = HyperparameterTuner(
@@ -429,7 +458,9 @@ def test_skips_environment_when_not_dict(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is not a dict"
+ )
 
  def test_passes_vpc_config(self):
  """Should set definition.vpc_config from model_trainer.networking._to_vpc_config()."""

sagemaker-train/src/sagemaker/train/tuner.py

@@ -1504,7 +1504,13 @@ def _build_training_job_definition(self, inputs):
  model_trainer.stopping_condition.max_wait_time_in_seconds
  )
 
- definition = HyperParameterTrainingJobDefinition(
+ # Propagate environment variables from ModelTrainer.
+ # Only include when it's a dict (even empty); omit otherwise so the
+ # Pydantic field stays Unassigned and is excluded during serialization.
+ env = model_trainer.environment
+
+ # Build base kwargs for the definition
+ definition_kwargs = dict(
  algorithm_specification=algorithm_spec,
  role_arn=model_trainer.role,
  input_data_config=input_data_config if input_data_config else None,
@@ -1515,10 +1521,11 @@ def _build_training_job_definition(self, inputs):
  enable_managed_spot_training=model_trainer.compute.enable_managed_spot_training,
  )
 
- # Pass through environment variables from model_trainer
- env = getattr(model_trainer, "environment", None)
- if env and isinstance(env, dict):
- definition.environment = env
+ # Include environment only when it's a dict (including empty).
+ if isinstance(env, dict):
+ definition_kwargs["environment"] = env
+
+ definition = HyperParameterTrainingJobDefinition(**definition_kwargs)
 
  # Pass through VPC config from model_trainer
  networking = getattr(model_trainer, "networking", None)

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

@@ -596,3 +596,73 @@ def test_build_training_job_definition_includes_spot_params(self):
  assert isinstance(
  definition.stopping_condition.max_wait_time_in_seconds, int
  ), "Max wait time should be set"
+
+ def test_build_training_job_definition_includes_environment_variables(self):
+ """Test that _build_training_job_definition includes environment variables.
+
+ This test verifies the fix for GitHub issue #5613 where tuning jobs were
+ missing environment variables that were set on the ModelTrainer.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment is not None, "Environment should not be None"
+ assert definition.environment == {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }, "Environment variables should match those set on ModelTrainer"
+
+ def test_build_training_job_definition_with_none_environment(self):
+ """Test that _build_training_job_definition handles None environment gracefully.
+
+ When environment is None, it should not be passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
+ from sagemaker.core.utils.utils import Unassigned
+
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = None
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert isinstance(definition.environment, Unassigned), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
+
+ def test_build_training_job_definition_with_empty_environment(self):
+ """Test that _build_training_job_definition passes through empty environment.
+
+ An empty dict is valid for the SageMaker API, so we pass it through as-is
+ rather than silently converting it to None.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {}
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )

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

@@ -405,8 +405,31 @@ def test_passes_environment_variables(self):
  definition = tuner._build_training_job_definition(inputs=None)
  assert definition.environment == {"MY_VAR": "value", "OTHER": "123"}
 
+ def test_passes_empty_environment(self):
+ """Should pass through empty dict environment as-is.
+
+ An empty dict is valid for the SageMaker API, so we pass it through
+ rather than silently converting it to None/Unassigned.
+ """
+ trainer = _mock_model_trainer(environment={})
+
+ tuner = HyperparameterTuner(
+ model_trainer=trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_hp_ranges(),
+ )
+
+ definition = tuner._build_training_job_definition(inputs=None)
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )
+
  def test_skips_environment_when_none(self):
- """Should not set environment when model_trainer.environment is None."""
+ """Should not set environment when model_trainer.environment is None.
+
+ When environment is None, it is not passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
  trainer = _mock_model_trainer(environment=None)
 
  tuner = HyperparameterTuner(
@@ -416,10 +439,16 @@ def test_skips_environment_when_none(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
 
  def test_skips_environment_when_not_dict(self):
- """Should not set environment when it's not a dict (e.g. MagicMock)."""
+ """Should not set environment when it's not a dict (e.g. MagicMock).
+
+ Non-dict values are not passed to the Pydantic constructor to avoid
+ validation errors. The field stays as Unassigned.
+ """
  trainer = _mock_model_trainer(environment=MagicMock())
 
  tuner = HyperparameterTuner(
@@ -429,7 +458,9 @@ def test_skips_environment_when_not_dict(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is not a dict"
+ )
 
  def test_passes_vpc_config(self):
  """Should set definition.vpc_config from model_trainer.networking._to_vpc_config()."""

sagemaker-train/src/sagemaker/train/tuner.py

@@ -1504,7 +1504,13 @@ def _build_training_job_definition(self, inputs):
  model_trainer.stopping_condition.max_wait_time_in_seconds
  )
 
- definition = HyperParameterTrainingJobDefinition(
+ # Propagate environment variables from ModelTrainer.
+ # Only include when it's a dict (even empty); omit otherwise so the
+ # Pydantic field stays Unassigned and is excluded during serialization.
+ env = model_trainer.environment
+
+ # Build base kwargs for the definition
+ definition_kwargs = dict(
  algorithm_specification=algorithm_spec,
  role_arn=model_trainer.role,
  input_data_config=input_data_config if input_data_config else None,
@@ -1515,10 +1521,11 @@ def _build_training_job_definition(self, inputs):
  enable_managed_spot_training=model_trainer.compute.enable_managed_spot_training,
  )
 
- # Pass through environment variables from model_trainer
- env = getattr(model_trainer, "environment", None)
- if env and isinstance(env, dict):
- definition.environment = env
+ # Include environment only when it's a dict (including empty).
+ if isinstance(env, dict):
+ definition_kwargs["environment"] = env
+
+ definition = HyperParameterTrainingJobDefinition(**definition_kwargs)
 
  # Pass through VPC config from model_trainer
  networking = getattr(model_trainer, "networking", None)

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

@@ -596,3 +596,73 @@ def test_build_training_job_definition_includes_spot_params(self):
  assert isinstance(
  definition.stopping_condition.max_wait_time_in_seconds, int
  ), "Max wait time should be set"
+
+ def test_build_training_job_definition_includes_environment_variables(self):
+ """Test that _build_training_job_definition includes environment variables.
+
+ This test verifies the fix for GitHub issue #5613 where tuning jobs were
+ missing environment variables that were set on the ModelTrainer.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment is not None, "Environment should not be None"
+ assert definition.environment == {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }, "Environment variables should match those set on ModelTrainer"
+
+ def test_build_training_job_definition_with_none_environment(self):
+ """Test that _build_training_job_definition handles None environment gracefully.
+
+ When environment is None, it should not be passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
+ from sagemaker.core.utils.utils import Unassigned
+
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = None
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert isinstance(definition.environment, Unassigned), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
+
+ def test_build_training_job_definition_with_empty_environment(self):
+ """Test that _build_training_job_definition passes through empty environment.
+
+ An empty dict is valid for the SageMaker API, so we pass it through as-is
+ rather than silently converting it to None.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {}
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )

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

@@ -405,8 +405,31 @@ def test_passes_environment_variables(self):
  definition = tuner._build_training_job_definition(inputs=None)
  assert definition.environment == {"MY_VAR": "value", "OTHER": "123"}
 
+ def test_passes_empty_environment(self):
+ """Should pass through empty dict environment as-is.
+
+ An empty dict is valid for the SageMaker API, so we pass it through
+ rather than silently converting it to None/Unassigned.
+ """
+ trainer = _mock_model_trainer(environment={})
+
+ tuner = HyperparameterTuner(
+ model_trainer=trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_hp_ranges(),
+ )
+
+ definition = tuner._build_training_job_definition(inputs=None)
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )
+
  def test_skips_environment_when_none(self):
- """Should not set environment when model_trainer.environment is None."""
+ """Should not set environment when model_trainer.environment is None.
+
+ When environment is None, it is not passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
  trainer = _mock_model_trainer(environment=None)
 
  tuner = HyperparameterTuner(
@@ -416,10 +439,16 @@ def test_skips_environment_when_none(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
 
  def test_skips_environment_when_not_dict(self):
- """Should not set environment when it's not a dict (e.g. MagicMock)."""
+ """Should not set environment when it's not a dict (e.g. MagicMock).
+
+ Non-dict values are not passed to the Pydantic constructor to avoid
+ validation errors. The field stays as Unassigned.
+ """
  trainer = _mock_model_trainer(environment=MagicMock())
 
  tuner = HyperparameterTuner(
@@ -429,7 +458,9 @@ def test_skips_environment_when_not_dict(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is not a dict"
+ )
 
  def test_passes_vpc_config(self):
  """Should set definition.vpc_config from model_trainer.networking._to_vpc_config()."""

sagemaker-train/src/sagemaker/train/tuner.py

@@ -1504,7 +1504,13 @@ def _build_training_job_definition(self, inputs):
  model_trainer.stopping_condition.max_wait_time_in_seconds
  )
 
- definition = HyperParameterTrainingJobDefinition(
+ # Propagate environment variables from ModelTrainer.
+ # Only include when it's a dict (even empty); omit otherwise so the
+ # Pydantic field stays Unassigned and is excluded during serialization.
+ env = model_trainer.environment
+
+ # Build base kwargs for the definition
+ definition_kwargs = dict(
  algorithm_specification=algorithm_spec,
  role_arn=model_trainer.role,
  input_data_config=input_data_config if input_data_config else None,
@@ -1515,10 +1521,11 @@ def _build_training_job_definition(self, inputs):
  enable_managed_spot_training=model_trainer.compute.enable_managed_spot_training,
  )
 
- # Pass through environment variables from model_trainer
- env = getattr(model_trainer, "environment", None)
- if env and isinstance(env, dict):
- definition.environment = env
+ # Include environment only when it's a dict (including empty).
+ if isinstance(env, dict):
+ definition_kwargs["environment"] = env
+
+ definition = HyperParameterTrainingJobDefinition(**definition_kwargs)
 
  # Pass through VPC config from model_trainer
  networking = getattr(model_trainer, "networking", None)

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

@@ -596,3 +596,73 @@ def test_build_training_job_definition_includes_spot_params(self):
  assert isinstance(
  definition.stopping_condition.max_wait_time_in_seconds, int
  ), "Max wait time should be set"
+
+ def test_build_training_job_definition_includes_environment_variables(self):
+ """Test that _build_training_job_definition includes environment variables.
+
+ This test verifies the fix for GitHub issue #5613 where tuning jobs were
+ missing environment variables that were set on the ModelTrainer.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment is not None, "Environment should not be None"
+ assert definition.environment == {
+ "FOO": "bar",
+ "RANDOM_STATE": "42",
+ }, "Environment variables should match those set on ModelTrainer"
+
+ def test_build_training_job_definition_with_none_environment(self):
+ """Test that _build_training_job_definition handles None environment gracefully.
+
+ When environment is None, it should not be passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
+ from sagemaker.core.utils.utils import Unassigned
+
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = None
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert isinstance(definition.environment, Unassigned), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
+
+ def test_build_training_job_definition_with_empty_environment(self):
+ """Test that _build_training_job_definition passes through empty environment.
+
+ An empty dict is valid for the SageMaker API, so we pass it through as-is
+ rather than silently converting it to None.
+ """
+ mock_trainer = _create_mock_model_trainer()
+ mock_trainer.environment = {}
+
+ tuner = HyperparameterTuner(
+ model_trainer=mock_trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_create_single_hp_range(),
+ )
+
+ definition = tuner._build_training_job_definition(None)
+
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )

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

@@ -405,8 +405,31 @@ def test_passes_environment_variables(self):
  definition = tuner._build_training_job_definition(inputs=None)
  assert definition.environment == {"MY_VAR": "value", "OTHER": "123"}
 
+ def test_passes_empty_environment(self):
+ """Should pass through empty dict environment as-is.
+
+ An empty dict is valid for the SageMaker API, so we pass it through
+ rather than silently converting it to None/Unassigned.
+ """
+ trainer = _mock_model_trainer(environment={})
+
+ tuner = HyperparameterTuner(
+ model_trainer=trainer,
+ objective_metric_name="accuracy",
+ hyperparameter_ranges=_hp_ranges(),
+ )
+
+ definition = tuner._build_training_job_definition(inputs=None)
+ assert definition.environment == {}, (
+ "Empty dict environment should be passed through as-is"
+ )
+
  def test_skips_environment_when_none(self):
- """Should not set environment when model_trainer.environment is None."""
+ """Should not set environment when model_trainer.environment is None.
+
+ When environment is None, it is not passed to the Pydantic constructor,
+ so the field stays as Unassigned (excluded from serialization).
+ """
  trainer = _mock_model_trainer(environment=None)
 
  tuner = HyperparameterTuner(
@@ -416,10 +439,16 @@ def test_skips_environment_when_none(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is None"
+ )
 
  def test_skips_environment_when_not_dict(self):
- """Should not set environment when it's not a dict (e.g. MagicMock)."""
+ """Should not set environment when it's not a dict (e.g. MagicMock).
+
+ Non-dict values are not passed to the Pydantic constructor to avoid
+ validation errors. The field stays as Unassigned.
+ """
  trainer = _mock_model_trainer(environment=MagicMock())
 
  tuner = HyperparameterTuner(
@@ -429,7 +458,9 @@ def test_skips_environment_when_not_dict(self):
  )
 
  definition = tuner._build_training_job_definition(inputs=None)
- assert _is_unassigned(definition.environment)
+ assert _is_unassigned(definition.environment), (
+ "Environment should be Unassigned when model_trainer.environment is not a dict"
+ )
 
  def test_passes_vpc_config(self):
  """Should set definition.vpc_config from model_trainer.networking._to_vpc_config()."""