`_deploy_for_ic` passes `instance_type` twice to `_deploy()` causing `TypeError` when deploying `CustomOrchestrator` as Inference Component

[lopezfelipe]

PySDK Version

• PySDK V2 (2.x)
• PySDK V3 (3.x)

Describe the bug

When deploying a CustomOrchestrator as an Inference Component — as documented in the Build and deploy AI inference workflows with new enhancements to the Amazon SageMaker Python SDK blog post and the Llama3.1-Mistral reference notebook — calling deploy() raises a TypeError: got multiple values for keyword argument 'instance_type'.

The issue is in _deploy_for_ic() (model_builder.py L4227-4237): instance_type and initial_instance_count are passed both as explicit keyword arguments and via **kwargs spread to self._deploy().

To reproduce

from sagemaker.serve.model_builder import ModelBuilder, SchemaBuilder
from sagemaker.serve.spec.inference_base import CustomOrchestrator
from sagemaker.core.inference_config import ResourceRequirements
from sagemaker.core.helper.session_helper import Session, get_execution_role

class MyOrchestrator(CustomOrchestrator):
 def __init__(self, endpoint_name, component_names):
 super().__init__()
 self.endpoint_name = endpoint_name
 self.component_names = component_names

 def handle(self, data, context=None):
 import json
 response = self.client.invoke_endpoint(
 EndpointName=self.endpoint_name,
 InferenceComponentName=self.component_names[0],
 Body=data if isinstance(data, (str, bytes)) else json.dumps(data),
 ContentType="application/json"
 )
 return json.loads(response["Body"].read())

role = get_execution_role()
sess = Session()

# Step 1: Build the orchestrator
orchestrator = ModelBuilder(
 inference_spec=MyOrchestrator(
 endpoint_name="my-existing-endpoint",
 component_names=["base-ic", "adapter-ic"],
 ),
 dependencies={"auto": False, "custom": ["cloudpickle"]},
 sagemaker_session=sess,
 role_arn=role,
 schema_builder=SchemaBuilder(sample_input="Test", sample_output={"generated_text": "test"}),
)

# Workaround for missing constructor fields (separate issue)
orchestrator.resource_requirements = ResourceRequirements(
 requests={"memory": 4096, "num_accelerators": 1, "copies": 1, "num_cpus": 2}
)
orchestrator.inference_component_name = "my-orchestrator-ic"

orchestrator.build()

# Step 2: Deploy — this triggers the bug
orchestrator.deploy(
 endpoint_name="my-existing-endpoint",
 custom_orchestrator_instance_type="ml.g6.12xlarge",
 initial_instance_count=1,
)

Expected behavior

deploy() should deploy the CustomOrchestrator as an Inference Component on the specified endpoint without error.

Screenshots or logs

│ 4226 │ │ │ # Create new IC via _deploy() │
│ ❱ 4227 │ │ │ return self._deploy( │
│ 4228 │ │ │ │ built_model=built_model, │
│ 4229 │ │ │ │ endpoint_name=endpoint_name, │
│ 4230 │ │ │ │ endpoint_type=EndpointType.INFERENCE_COMPONENT_BASED, │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
TypeError: sagemaker.serve.model_builder.ModelBuilder._deploy() got multiple values for keyword argument 
'instance_type'

Full traceback:

/opt/conda/lib/python3.12/site-packages/sagemaker/serve/model_builder.py:6189 in deploy
│ ❱ 6189 │ │ │ │ │ │ self._deploy_for_ic(
│ 6190 │ │ │ │ │ │ │ ic_data=custom_orchestrator,
│ 6191 │ │ │ │ │ │ │ container_timeout_in_seconds=container_timeout_in_seconds,
│ 6192 │ │ │ │ │ │ │ instance_type=custom_orchestrator_instance_type or instance_type,

/opt/conda/lib/python3.12/site-packages/sagemaker/serve/model_builder.py:4227 in _deploy_for_ic
│ ❱ 4227 │ │ │ return self._deploy(
│ 4228 │ │ │ │ built_model=built_model,
│ 4229 │ │ │ │ endpoint_name=endpoint_name,
│ 4230 │ │ │ │ endpoint_type=EndpointType.INFERENCE_COMPONENT_BASED,

TypeError: sagemaker.serve.model_builder.ModelBuilder._deploy() got multiple values for keyword argument 'instance_type'

System information

• SageMaker Python SDK version: sagemaker-serve 1.20.0 (SDK V3)
• Framework name: SageMaker Distribution (SMD) container
• Framework version: sagemaker-distribution-prod:3.2.0-cpu
• Python version: 3.12
• CPU or GPU: GPU (ml.g6.2xlarge endpoint)
• Custom Docker image (Y/N): N

Additional context

Root cause analysis:

In deploy() (L6189-6196), _deploy_for_ic is called with instance_type as an explicit kwarg:

self._deploy_for_ic(
 ic_data=custom_orchestrator,
 container_timeout_in_seconds=container_timeout_in_seconds,
 instance_type=custom_orchestrator_instance_type or instance_type, # explicit
 initial_instance_count=custom_orchestrator_initial_instance_count or initial_instance_count, # explicit
 endpoint_name=endpoint_name,
 **kwargs,
)

Then in _deploy_for_ic() (L4227-4237):

def _deploy_for_ic(self, ic_data, endpoint_name, **kwargs):
 ...
 return self._deploy(
 built_model=built_model,
 endpoint_name=endpoint_name,
 endpoint_type=EndpointType.INFERENCE_COMPONENT_BASED,
 resources=resource_requirements,
 inference_component_name=ic_name,
 instance_type=kwargs.get("instance_type", self.instance_type), # extracted from kwargs
 initial_instance_count=kwargs.get("initial_instance_count", 1), # extracted from kwargs
 **kwargs, # ← kwargs STILL contains instance_type → duplicate!
 )

instance_type is extracted from kwargs on one line, then **kwargs is spread on the next — passing the same key twice to _deploy().