08. Creating and Using an Endpoint

Creating and Using Endpoints

You've just learned a lot about how to use SageMaker to deploy a model and perform inference on some data. Now is a good time to review some of the key steps that we've covered. You have experience processing data and creating estimators/models, so I'll focus on what you've learned about endpoints.

An endpoint, in this case, is a URL that allows an application and a model to speak to one another.

Endpoint steps

  • You can start an endpoint by calling .deploy() on an estimator and passing in some information about the instance. 
xgb_predictor = xgb.deploy(initial_instance_count = 1, instance_type = 'ml.m4.xlarge')
  • Then, you need to tell your endpoint, what type of data it expects to see as input (like .csv). 
from sagemaker.predictor import csv_serializer

xgb_predictor.content_type = 'text/csv'
xgb_predictor.serializer = csv_serializer
  • Then, perform inference; you can pass some data as the "Body" of a message, to an endpoint and get a response back! 
response = runtime.invoke_endpoint(EndpointName = xgb_predictor.endpoint,   # The name of the endpoint we created
                                       ContentType = 'text/csv',                     # The data format that is expected
                                       Body = ','.join([str(val) for val in test_bow]).encode('utf-8'))
  • The inference data is stored in the "Body" of the response, and can be retrieved: 
response = response['Body'].read().decode('utf-8')
print(response)
  • Finally, do not forget to shut down your endpoint when you are done using it. 
xgb_predictor.delete_endpoint()

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