How to reuse SageMaker endpoint created by JumpStartModel.deploy()

Question

I have deployed a SageMaker JumpStartModel using this code from a sample notebook:

```
from sagemaker.jumpstart.model import JumpStartModel
model_id = "meta-textgeneration-llama-2-7b"
pretrained_model = JumpStartModel(model_id=model_id)
pretrained_predictor = pretrained_model.deploy()
```

However my Jupyter kernel has died and no longer knows the `pretrained_predictor` variable. How can I re-create the `pretrained_predictor` from an existing endpoint? I know it's name, for example `meta-textgeneration-llama-2-7b-2023-09-10-12-34-56-789` and its ARN if needed.

Answer

Hi,

To re-use an existing SageMaker endpoit, you have to use the UpdateEndpoint API: see https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html

```
Deploys the new EndpointConfig specified in the request, switches to using newly created 
endpoint, and then deletes resources provisioned for the endpoint using the previous 
EndpointConfig (there is no availability loss).

When SageMaker receives the request, it sets the endpoint status to Updating. After 
updating the endpoint, it sets the status to InService. To check the status of an endpoint, 
use the DescribeEndpoint API.

Note
You must not delete an EndpointConfig in use by an endpoint that is live or while the 
UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. 
To update an endpoint, you must create a new EndpointConfig.

If you delete the EndpointConfig of an endpoint that is active or being created or 
updated you may lose visibility into the instance type the endpoint is using. The endpoint 
must be deleted in order to stop incurring charges.
```

For Python, the API is detailled here: https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker/client/update_endpoint.html

Best,

DIdier

Answer

you can use the predictor class to do this.

How to reuse SageMaker endpoint created by JumpStartModel.deploy()

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