chore: emit warning when no instance specific gated training env var is available, and raise exception when accept_eula flag is not supplied

src/sagemaker/jumpstart/artifacts/environment_variables.py

@@ -120,6 +120,13 @@ def _retrieve_default_environment_variables(
                 instance_type=instance_type,
             )
 
+            if gated_model_env_var is None and model_specs.gated_bucket:
+                raise ValueError(
+                    f"'{model_id}' does not support {instance_type} instance type for training. "
+                    "Please use one of the following instance types: "
+                    f"{', '.join(model_specs.supported_training_instance_types)}."
+                )
+
             if gated_model_env_var is not None:
                 default_environment_variables.update(
                     {SAGEMAKER_GATED_MODEL_S3_URI_TRAINING_ENV_VAR_KEY: gated_model_env_var}

tests/unit/sagemaker/environment_variables/jumpstart/test_default.py

@@ -18,6 +18,7 @@
 import pytest
 
 from sagemaker import environment_variables
+from sagemaker.jumpstart.utils import get_jumpstart_gated_content_bucket
 
 from tests.unit.sagemaker.jumpstart.utils import get_spec_from_base_spec, get_special_model_spec
 
@@ -177,6 +178,46 @@ def test_jumpstart_sdk_environment_variables(patched_get_model_specs):
         )
 
 
+@patch("sagemaker.jumpstart.accessors.JumpStartModelsAccessor.get_model_specs")
+def test_jumpstart_sdk_environment_variables_no_gated_env_var_available(patched_get_model_specs):
+
+    patched_get_model_specs.side_effect = get_special_model_spec
+
+    model_id = "gemma-model"
+    region = "us-west-2"
+
+    # assert that unsupported instance types raise an exception
+    with pytest.raises(ValueError) as e:
+        environment_variables.retrieve_default(
+            region=region,
+            model_id=model_id,
+            model_version="*",
+            include_aws_sdk_env_vars=False,
+            sagemaker_session=mock_session,
+            instance_type="ml.p3.2xlarge",
+            script="training",
+        )
+    assert (
+        str(e.value) == "'gemma-model' does not support ml.p3.2xlarge instance type for "
+        "training. Please use one of the following instance types: "
+        "ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.p4d.24xlarge."
+    )
+
+    # assert that supported instance types succeed
+    assert {
+        "SageMakerGatedModelS3Uri": f"s3://{get_jumpstart_gated_content_bucket(region)}/"
+        "huggingface-training/g5/v1.0.0/train-huggingface-llm-gemma-7b-instruct.tar.gz"
+    } == environment_variables.retrieve_default(
+        region=region,
+        model_id=model_id,
+        model_version="*",
+        include_aws_sdk_env_vars=False,
+        sagemaker_session=mock_session,
+        instance_type="ml.g5.24xlarge",
+        script="training",
+    )
+
+
 @patch("sagemaker.jumpstart.accessors.JumpStartModelsAccessor.get_model_specs")
 def test_jumpstart_sdk_environment_variables_instance_type_overrides(patched_get_model_specs):