在AWS SageMaker上重新托管经过训练的模型

Question

I have started exploring AWS SageMaker starting with these examples provided by AWS . I then made some modifications to this particular setup so that it uses the data from my use case for training.

Now, as I continue to work on this model and tuning, after I delete the inference endpoint once, I would like to be able to recreate the same endpoint -- even after stopping and restarting the notebook instance (so the notebook / kernel session is no longer valid) -- using the already trained model artifacts that gets uploaded to S3 under /output folder.

Now I cannot simply jump directly to this line of code:

bt_endpoint = bt_model.deploy(initial_instance_count = 1,instance_type = 'ml.m4.xlarge')

I did some searching -- including amazon's own example of hosting pre-trained models , but I am a little lost. I would appreciate any guidance, examples, or documentation that I could emulate and adapt to my case.

Answer 1

Your comment is correct - you can re-create an Endpoint given an existing EndpointConfiguration. This can be done via the console, the AWS CLI, or the SageMaker boto client.

• https://docs.aws.amazon.com/cli/latest/reference/sagemaker/create-endpoint.html
• https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_endpoint