Check prediction results - Amazon SageMaker

Check prediction results

There are several ways you can check predictions results from your asynchronous endpoint. Some options are:

  1. Amazon SNS topics.

  2. Check for outputs in your Amazon S3 bucket.

Amazon SNS Topics

Amazon SNS is a notification service for messaging-oriented applications, with multiple subscribers requesting and receiving "push" notifications of time-critical messages via a choice of transport protocols, including HTTP, Amazon SQS, and email. Amazon SageMaker Asynchronous Inference posts notifications when you create an endpoint with CreateEndpointConfig and specify an Amazon SNS topic.


In order to receive Amazon SNS notifications, your IAM role must have sns:Publish permissions. See the Prerequisites for information on requirements you must satisfy to use Asynchronous Inference.

To use Amazon SNS to check prediction results from your asynchronous endpoint, you first need to create a topic, subscribe to the topic, confirm your subscription to the topic, and note the Amazon Resource Name (ARN) of that topic. For detailed information on how to create, subscribe, and find the Amazon ARN of an Amazon SNS topic, see Configuring Amazon SNS.

Provide the Amazon SNS topic ARN(s) in the AsyncInferenceConfig field when you create an endpoint configuration with CreateEndpointConfig. You can specify both an Amazon SNS ErrorTopic and an SuccessTopic.

import boto3 sagemaker_client = boto3.client('sagemaker', region_name=<aws_region>) sagemaker_client.create_endpoint_config( EndpointConfigName=<endpoint_config_name>, # You specify this name in a CreateEndpoint request. # List of ProductionVariant objects, one for each model that you want to host at this endpoint. ProductionVariants=[ { "VariantName": "variant1", # The name of the production variant. "ModelName": "model_name", "InstanceType": "ml.m5.xlarge", # Specify the compute instance type. "InitialInstanceCount": 1 # Number of instances to launch initially. } ], AsyncInferenceConfig={ "OutputConfig": { # Location to upload response outputs when no location is provided in the request. "S3OutputPath": "s3://<bucket>/<output_directory>" "NotificationConfig": { "SuccessTopic": "arn:aws:sns:aws-region:account-id:topic-name", "ErrorTopic": "arn:aws:sns:aws-region:account-id:topic-name", } } } )

After creating your endpoint and invoking it, you receive a notification from your Amazon SNS topic. For example, if you subscribed to receive email notifications from your topic, you receive an email notification every time you invoke your endpoint. The following example shows the JSON content of a successful invocation email notification.

{ "awsRegion":"us-east-1", "eventTime":"2022-01-25T22:46:00.608Z", "receivedTime":"2022-01-25T22:46:00.455Z", "invocationStatus":"Completed", "requestParameters":{ "contentType":"text/csv", "endpointName":"<example-endpoint>", "inputLocation":"s3://<bucket>/<input-directory>/input-data.csv" }, "responseParameters":{ "contentType":"text/csv; charset=utf-8", "outputLocation":"s3://<bucket>/<output_directory>/prediction.out" }, "inferenceId":"11111111-2222-3333-4444-555555555555", "eventVersion":"1.0", "eventSource":"aws:sagemaker", "eventName":"InferenceResult" }

Check Your S3 Bucket

When you invoke an endpoint with InvokeEndpointAsync, it returns a response object. You can use the response object to get the Amazon S3 URI where your output is stored. With the output location, you can use a SageMaker Python SDK SageMaker session class to programmatically check for on an output.

The following stores the output dictionary of InvokeEndpointAsync as a variable named response. With the response variable, you then get the Amazon S3 output URI and store it as a string variable called output_location.

import uuid import boto3 sagemaker_runtime = boto3.client("sagemaker-runtime", region_name=<aws_region>) # Specify the S3 URI of the input. Here, a single SVM sample input_location = "s3://bucket-name/test_point_0.libsvm" response = sagemaker_runtime.invoke_endpoint_async( EndpointName='<endpoint-name>', InputLocation=input_location, InferenceId=str(uuid.uuid4()), ContentType="text/libsvm" #Specify the content type of your data ) output_location = response['OutputLocation'] print(f"OutputLocation: {output_location}")

For information about supported content types, see Common Data Formats for Inference.

With the Amazon S3 output location, you can then use a SageMaker Python SDK SageMaker Session Class to read in Amazon S3 files. The following code example shows how to create a function (get_ouput) that repeatedly attempts to read a file from the Amazon S3 output location:

import sagemaker import urllib, time from botocore.exceptions import ClientError sagemaker_session = sagemaker.session.Session() def get_output(output_location): output_url = urllib.parse.urlparse(output_location) bucket = output_url.netloc key = output_url.path[1:] while True: try: return sagemaker_session.read_s3_file( bucket=output_url.netloc, key_prefix=output_url.path[1:]) except ClientError as e: if e.response['Error']['Code'] == 'NoSuchKey': print("waiting for output...") time.sleep(2) continue raise output = get_output(output_location) print(f"Output: {output}")