Master

src/sagemaker/jumpstart/accessors.py

@@ -25,6 +25,7 @@
 from sagemaker.jumpstart.hub.utils import (
     construct_hub_model_arn_from_inputs,
     construct_hub_model_reference_arn_from_inputs,
+    generate_hub_arn_for_init_kwargs,
 )
 from sagemaker.jumpstart.constants import JUMPSTART_DEFAULT_REGION_NAME
 from sagemaker.session import Session
@@ -291,6 +292,10 @@ def get_model_specs(
         # Users only input model id, not contentType, so first try to describe with ModelReference, then with Model
         if hub_arn:
             try:
+                hub_arn = generate_hub_arn_for_init_kwargs(
+                    hub_name=hub_arn, region=region, session=sagemaker_session
+                )
+
                 hub_model_arn = construct_hub_model_reference_arn_from_inputs(
                     hub_arn=hub_arn, model_name=model_id, version=version
                 )

src/sagemaker/jumpstart/estimator.py

@@ -693,7 +693,7 @@ def fit(
                 accept the end-user license agreement (EULA) that some
                 models require. (Default: None).
         """
-        self.model_access_config = get_model_access_config(accept_eula)
+        self.model_access_config = get_model_access_config(accept_eula, self.environment)
         self.hub_access_config = get_hub_access_config(
             hub_content_arn=self.init_kwargs.get("model_reference_arn", None)
         )

src/sagemaker/jumpstart/utils.py

@@ -1641,8 +1641,14 @@ def remove_env_var_from_estimator_kwargs_if_accept_eula_present(
         init_kwargs (dict): Dictionary of kwargs when Estimator is instantiated.
         accept_eula (Optional[bool]): Whether or not the EULA was accepted, optionally passed in to Estimator.fit().
     """
-    if accept_eula is not None and init_kwargs["environment"]:
-        del init_kwargs["environment"][constants.SAGEMAKER_GATED_MODEL_S3_URI_TRAINING_ENV_VAR_KEY]
+    if accept_eula is not None and init_kwargs.get("environment") is not None:
+        if (
+            constants.SAGEMAKER_GATED_MODEL_S3_URI_TRAINING_ENV_VAR_KEY
+            in init_kwargs["environment"]
+        ):
+            del init_kwargs["environment"][
+                constants.SAGEMAKER_GATED_MODEL_S3_URI_TRAINING_ENV_VAR_KEY
+            ]
 
 
 def get_hub_access_config(hub_content_arn: Optional[str]):
@@ -1659,16 +1665,24 @@ def get_hub_access_config(hub_content_arn: Optional[str]):
     return hub_access_config
 
 
-def get_model_access_config(accept_eula: Optional[bool]):
+def get_model_access_config(accept_eula: Optional[bool], environment: Optional[dict]):
     """Get access configs
 
     Args:
         accept_eula (Optional[bool]): Whether or not the EULA was accepted, optionally passed in to Estimator.fit().
     """
+    env_var_eula = environment.get("accept_eula")
+    if env_var_eula and accept_eula is not None:
+        raise ValueError(
+            "Cannot pass in both accept_eula and environment variables. "
+            "Please remove the environment variable and pass in the accept_eula parameter."
+        )
+
+    model_access_config = None
+    if env_var_eula is not None:
+        model_access_config = {"AcceptEula": True if env_var_eula == "true" else False}
     if accept_eula is not None:
         model_access_config = {"AcceptEula": accept_eula}
-    else:
-        model_access_config = None
 
     return model_access_config
 

tests/integ/sagemaker/jumpstart/private_hub/estimator/test_jumpstart_private_hub_estimator.py

@@ -170,6 +170,42 @@ def test_jumpstart_hub_gated_estimator_with_eula(setup, add_model_references):
     assert response is not None
 
 
+def test_jumpstart_hub_gated_estimator_with_eula_env_var(setup, add_model_references):
+
+    model_id, model_version = "meta-textgeneration-llama-2-7b", "*"
+
+    estimator = JumpStartEstimator(
+        model_id=model_id,
+        hub_name=os.environ[ENV_VAR_JUMPSTART_SDK_TEST_HUB_NAME],
+        environment={
+            "accept_eula": "true",
+        },
+        tags=[{"Key": JUMPSTART_TAG, "Value": os.environ[ENV_VAR_JUMPSTART_SDK_TEST_SUITE_ID]}],
+    )
+
+    estimator.fit(
+        inputs={
+            "training": f"s3://{get_jumpstart_content_bucket(JUMPSTART_DEFAULT_REGION_NAME)}/"
+            f"{get_training_dataset_for_model_and_version(model_id, model_version)}",
+        },
+    )
+
+    predictor = estimator.deploy(
+        tags=[{"Key": JUMPSTART_TAG, "Value": os.environ[ENV_VAR_JUMPSTART_SDK_TEST_SUITE_ID]}],
+        role=get_sm_session().get_caller_identity_arn(),
+        sagemaker_session=get_sm_session(),
+    )
+
+    payload = {
+        "inputs": "some-payload",
+        "parameters": {"max_new_tokens": 256, "top_p": 0.9, "temperature": 0.6},
+    }
+
+    response = predictor.predict(payload, custom_attributes="accept_eula=true")
+
+    assert response is not None
+
+
 def test_jumpstart_hub_gated_estimator_without_eula(setup, add_model_references):
 
     model_id, model_version = "meta-textgeneration-llama-2-7b", "*"