CleanupGuide

Cleanup Guide

To avoid charges for resources you no longer need when you're done with this workshop, you can delete them or, in the case of your notebook instance, stop them. Here are the resources you should consider:

• Endpoints: these are the clusters of one or more instances serving inferences from your models. If you did not delete them from within a notebook, you can delete them via the SageMaker console. To do so:

  • Click the Endpoints link in the left panel.

  • Then, for each endpoint, click the radio button next to it, then select Delete from the Actions drop down menu.

  • You can follow a similar procedure to delete the related Models and Endpoint configurations.

• Notebook instance: you have two options if you do not want to keep the notebook instance running. If you would like to save it for later, you can stop rather than deleting it.

  • To stop a notebook instance: click the Notebook instances link in the left pane of the SageMaker console home page. Next, click the Stop link under the 'Actions' column to the left of your notebook instance's name. After the notebook instance is stopped, you can start it again by clicking the Start link. Keep in mind that if you stop rather than delete it, you will be charged for the storage associated with it.

  • To delete a notebook instance: first stop it per the instruction above. Next, click the radio button next to your notebook instance, then select Delete from the Actions drop down menu.

• S3 Bucket:

• If you retain the data created for this workshop, you will be charged for storage. We used the default SageMaker bucket for this workshop, which may have already existed if you were previously using SageMaker.

  • To avoid these charges if you no longer wish to use the bucket, you may delete it if you have not used SageMaker before.

  • To delete the bucket, go to the S3 service console, and locate your bucket's name in the bucket table, which will be something like sagemaker-{aws-region}-{aws-account-number}.

  • Next, click in the bucket table row for your bucket to highlight the table row. At the top of the table, the Delete Bucket button should now be enabled, so click it and then click the Confirm button in the resulting pop-up to complete the deletion.

• If you have used SageMaker before,

  • enter the S3 bucket, which will be named something like sagemaker-{aws-region}-{aws-account-number}
  • delete the folder ml-immersion-day
  • delete any folders beginning with tf-keras-sentiment-*

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