How can I deploy a re-trained Sagemaker model to an endpoint? - python-3.x

Question

With an sagemaker.estimator.Estimator, I want to re-deploy a model after retraining (calling fit with new data).
When I call this
estimator.deploy(initial_instance_count=1, instance_type='ml.m5.xlarge')
I get an error
botocore.exceptions.ClientError: An error occurred (ValidationException)
when calling the CreateEndpoint operation:
Cannot create already existing endpoint "arn:aws:sagemaker:eu-east-
1:1776401913911:endpoint/zyx".
Apparently I want to use functionality like UpdateEndpoint. How do I access that functionality from this API?

Answer 1

Yes, under the hood the model.deploy creates a model, an endpoint configuration and an endpoint. When you call again the method from an already-deployed, trained estimator it will create an error because a similarly-configured endpoint is already deployed. What I encourage you to try:
use the update_endpoint=True parameter. From the SageMaker SDK doc:
"Additionally, it is possible to deploy a different endpoint configuration, which links to your model, to an already existing
SageMaker endpoint. This can be done by specifying the existing
endpoint name for the endpoint_name parameter along with the
update_endpoint parameter as True within your deploy() call."
Alternatively, if you want to create a separate model you can specify a new model_name in your deploy

Answer 2

update_endpoint has been deprecated since AFAIK . To re-create the UpdateEndpoint functionality from this API itself and deploy a newly fit training job to an existing endpoint , we could do something like this (this example uses the sagemaker sklearn API):
from sagemaker.sklearn.estimator import SKLearn
sklearn_estimator = SKLearn(
entry_point=model.py,
instance_type=<instance_type>,
framework_version=<framework_version>,
role=<role>,
dependencies=[
<comma seperated names of files>
],
hyperparameters={
'key_1':value,
'key_2':value,
...
}
)
sklearn_estimator.fit()
sm_client = boto3.client('sagemaker')
# Create the model
sklearn_model = sklearn_estimator.create_model()
# Define an endpoint config and an endpoint
endpoint_config_name = 'endpoint-' + datetime.utcnow().strftime("%Y%m%d%H%m%s")
current_endpoint = endpoint_config_name
# From the Model : create the endpoint config and the endpoint
sklearn_model.deploy(
initial_instance_count=<count>,
instance_type=<instance_type>,
endpoint_name=current_endpoint
)
# Update the existing endpoint if it exists or create a new one
try:
sm_client.update_endpoint(
EndpointName=DESIRED_ENDPOINT_NAME, # The Prod/Existing Endpoint Name
EndpointConfigName=endpoint_config_name
)
except Exception as e:
try:
sm_client.create_endpoint(
EndpointName=DESIRED_ENDPOINT_NAME, # The Prod Endpoint name
EndpointConfigName=endpoint_config_name
)
except Exception as e:
logger.info(e)
sm_client.delete_endpoint(EndpointName=current_endpoint)