Add target_container_hostname to base predictor

src/sagemaker/base_predictor.py

@@ -154,6 +154,7 @@ def predict(
         inference_id=None,
         custom_attributes=None,
         component_name: Optional[str] = None,
+        target_container_hostname=None,
     ):
         """Return the inference from the specified endpoint.
 
@@ -188,6 +189,9 @@ def predict(
                 function (Default: None).
             component_name (str): Optional. Name of the Amazon SageMaker inference component
                 corresponding the predictor.
+            target_container_hostname (str): Optional. If the endpoint hosts multiple containers
+                and is configured to use direct invocation, this parameter specifies the host name
+                of the container to invoke. (Default: None).
 
         Returns:
             object: Inference for the given input. If a deserializer was specified when creating
@@ -203,6 +207,7 @@ def predict(
             target_variant=target_variant,
             inference_id=inference_id,
             custom_attributes=custom_attributes,
+            target_container_hostname=target_container_hostname,
         )
 
         inference_component_name = component_name or self._get_component_name()

tests/unit/test_predictor.py

@@ -135,6 +135,29 @@ def test_predict_call_with_inference_id():
     assert result == RETURN_VALUE
 
 
+def test_predict_call_with_target_container_hostname():
+    sagemaker_session = empty_sagemaker_session()
+    predictor = Predictor(ENDPOINT, sagemaker_session)
+
+    data = "untouched"
+    result = predictor.predict(data, target_container_hostname="test_target_container_hostname")
+
+    assert sagemaker_session.sagemaker_runtime_client.invoke_endpoint.called
+
+    expected_request_args = {
+        "Accept": DEFAULT_ACCEPT,
+        "Body": data,
+        "ContentType": DEFAULT_CONTENT_TYPE,
+        "EndpointName": ENDPOINT,
+        "TargetContainerHostname": "test_target_container_hostname",
+    }
+
+    _, kwargs = sagemaker_session.sagemaker_runtime_client.invoke_endpoint.call_args
+    assert kwargs == expected_request_args
+
+    assert result == RETURN_VALUE
+
+
 def test_multi_model_predict_call():
     sagemaker_session = empty_sagemaker_session()
     predictor = Predictor(ENDPOINT, sagemaker_session)