Invoke an Asynchronous Endpoint - Amazon SageMaker
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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 following fields in your request:

  • For InputLocation, specify the location of your inference data.

  • For EndpointName, specify the name of your endpoint.

  • (Optional) For InvocationTimeoutSeconds, you can set the max timeout for the requests. You can set this value to a maximum of 3600 seconds (one hour) on a per-request basis. If you don't specify this field in your request, by default the request times out at 15 minutes.

# 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,
                            InvocationTimeoutSeconds=3600)

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.