sagemaker-python-sdk
sagemaker-python-sdk copied to clipboard
update_endpoint does not support serverless_inference_config
Currently you can deploy a serverless endpoint with,
predictor = model.deploy(serverless_inference_config=ServerlessInferenceConfig())
however serverless_inference_config
is not supported when you call predictor.update_endpoint
you have to use a standard endpoint;
predictor.update_endpoint(model_name=model.name, instance_type="ml.m4.xlarge", initial_instance_count=1)`
. Ideally you would be able to call,
predictor.update_endpoint(model_name=model.name, serverless_inference_config=ServerlessInferenceConfig())
to keep a serverless deployment.
Similarly, it'd be great if we can add serverless_inference_config
kwargs in sagemaker.session.Session.create_endpoint_config
class Session(object):
def create_endpoint_config(
self,
name,
model_name,
initial_instance_count,
instance_type,
accelerator_type=None,
serverless_inference_config=None, # TODO: <--- add this
tags=None,
kms_key=None,
data_capture_config_dict=None,
volume_size=None,
model_data_download_timeout=None,
container_startup_health_check_timeout=None,
explainer_config_dict=None,
):
...