Invoke an Asynchronous Endpoint - Amazon SageMaker

Invoke an Asynchronous Endpoint

Get inferences from the model hosted at your asynchronous endpoint with InvokeEndpointAsync.

Note

If you have not done so already, upload your inference data (e.g., machine learning model, sample data) to Amazon S3.

Specify the location of your inference data in the InputLocation field and the name of your endpoint for EndpointName:

# Create a low-level client representing Amazon SageMaker Runtime sagemaker_runtime = boto3.client("sagemaker-runtime", region_name=<aws_region>) # Specify the location of the input. Here, a single SVM sample input_location = "s3://bucket-name/test_point_0.libsvm" # The name of the endpoint. The name must be unique within an AWS Region in your AWS account. endpoint_name='<endpoint-name>' # After you deploy a model into production using SageMaker hosting # services, your client applications use this API to get inferences # from the model hosted at the specified endpoint. response = sagemaker_runtime.invoke_endpoint_async( EndpointName=endpoint_name, InputLocation=input_location)

You receive a response as a JSON string with your request ID and the name of the Amazon S3 bucket that will have the response to the API call after it is processed.