mypy-boto3-sagemaker-runtime

import boto3 from mypy_boto3_sagemaker_runtime.client import SagemakerRuntimeClient from mypy_boto3_sagemaker_runtime.type_defs import InvokeEndpointOutputTypeDef import os # Get a type-hinted SageMaker Runtime client client: SagemakerRuntimeClient = boto3.client("sagemaker-runtime") # Example: Invoke a SageMaker endpoint # For a real use case, replace 'your-endpoint-name' with an actual endpoint # and 'application/json' with the correct ContentType for your model. endpoint_name = os.environ.get("SAGEMAKER_ENDPOINT_NAME", "your-endpoint-name") content_type = "application/json" body = b'{"instances": [[1.0, 2.0, 3.0]]}' # Example JSON payload try: # The response object is type-hinted by InvokeEndpointOutputTypeDef response: InvokeEndpointOutputTypeDef = client.invoke_endpoint( EndpointName=endpoint_name, ContentType=content_type, Body=body ) # The 'Body' key in the response is a StreamingBody object response_body = response['Body'].read().decode('utf-8') print(f"Successfully invoked endpoint '{endpoint_name}'.") print(f"Response Status Code: {response['ResponseMetadata']['HTTPStatusCode']}") print(f"Response Body: {response_body}") except client.exceptions.ValidationError as e: print(f"Validation error: {e}") print("Please ensure 'SAGEMAKER_ENDPOINT_NAME' environment variable is set to a valid endpoint, and 'Body' matches its expected format.") except Exception as e: print(f"An unexpected error occurred: {e}") print("This might happen if the endpoint does not exist or credentials are not configured.")