3 Misc Cleanup

Misc and Cleanup

In this last notebook we show you how to use various APIs in relation to your endpoint and inference components on SageMaker. We will also clean up the resources you created in the previous notebooks. This is the 5th and last notebook of the series of 5 notebooks that will will show you other apis available and clean up the artifacts created.

Tested using the Python 3 (Data Science) kernel on SageMaker Studio and conda_python3 kernel on SageMaker Notebook Instance.

This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.

General Setup

Install dependencies

Upgrade the SageMaker Python SDK.

Import libraries

Set configurations

Set variables for endpoint name and inference component names set in the previous notebooks.

We first by creating the objects we will need for our notebook. In particular, the boto3 library to create the various clients we will need to interact with SageMaker and other variables that will be referenced later in our notebook.

Listing and Describing Endpoints and Inference Components

Deleting Inference Components and Endpoint

Note you will need to delete all inference components before you can delete an endpoint. Deletions of inference components are an asynch process.

Notebook CI Test Results

This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.