aws_lambda_to_sagemaker.py

import os
import io
import boto3
import json
import csv

# grab environment variables
ENDPOINT_NAME = 'xgboost-2020-02-01-16-28-38-417'
runtime= boto3.client('runtime.sagemaker')

def lambda_handler(event, context):
    print("Received event: " + json.dumps(event, indent=2))
    
    body = event.get('body')
    body = json.loads(body) if isinstance(body, str) else body
    payload = body['data']
    
    response = runtime.invoke_endpoint(EndpointName=ENDPOINT_NAME,
                                       ContentType='text/csv',
                                       Body=payload)
    
    print(response)
    result = json.loads(response['Body'].read().decode())
    print("result=", result)
    predicted_label = int(result)
    
    print('nothing left but return')

    # return format required by API Gateway's HTTP API
    # see: https://aws.amazon.com/blogs/compute/announcing-http-apis-for-amazon-api-gateway/
    # see: https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-create-api-as-simple-proxy-for-lambda.html
    return {
        'statusCode': 200,
        'body': json.dumps({'predicted': predicted_label})
    }

This is the lambda function I used to expose an HTTP endpoint for the mnist handwritten digits model created in the tutorial at https://docs.aws.amazon.com/sagemaker/latest/dg/getting-started-client-app.html