# Test pre-annotation and post-annotation Lambda functions **Note** Amazon SageMaker Ground Truth is no longer open to new customers. Existing customers can continue to use the service as normal. AWS continues to invest in security and availability improvements for Ground Truth, but we do not plan to introduce new features. You can test your pre-annotation and post annotation Lambda functions in the Lambda console. If you are a new user of Lambda, you can learn how to test, or *invoke*, your Lambda functions in the console using the [Create a Lambda function](https://docs.aws.amazon.com/lambda/latest/dg/getting-started-create-function.html#gettingstarted-zip-function) tutorial with the console in the AWS Lambda Developer Guide. You can use the sections on this page to learn how to test the Ground Truth pre-annotation and post-annotation templates provided through an AWS Serverless Application Repository (SAR). **Topics** + [Prerequisites](#sms-custom-templates-step3-lambda-test-pre) + [Test the Pre-annotation Lambda Function](#sms-custom-templates-step3-lambda-test-pre-annotation) + [Test the Post-Annotation Lambda Function](#sms-custom-templates-step3-lambda-test-post-annotation) ## Prerequisites You must do the following to use the tests described on this page. + You need access to the Lambda console, and you need permission to create and invoke Lambda functions. To learn how to set up these permissions, see [Grant Permission to Create and Select an AWS Lambda Function](sms-custom-templates-step3-lambda-permissions.md#sms-custom-templates-step3-postlambda-create-perms). + If you have not deployed the Ground Truth SAR recipe, use the procedure in [Create Lambda functions using Ground Truth templates](sms-custom-templates-step3-lambda-create.md) to do so. + To test the post-annotation Lambda function, you must have a data file in Amazon S3 with sample annotation data. For a simple test, you can copy and paste the following code into a file and save it as `sample-annotations.json` and [upload this file to Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/upload-objects.html). Note the S3 URI of this file—you need this information to configure the post-annotation Lambda test. ``` [{"datasetObjectId":"0","dataObject":{"content":"To train a machine learning model, you need a large, high-quality, labeled dataset. Ground Truth helps you build high-quality training datasets for your machine learning models."},"annotations":[{"workerId":"private.us-west-2.0123456789","annotationData":{"content":"{\"crowd-entity-annotation\":{\"entities\":[{\"endOffset\":8,\"label\":\"verb\",\"startOffset\":3},{\"endOffset\":27,\"label\":\"adjective\",\"startOffset\":11},{\"endOffset\":33,\"label\":\"object\",\"startOffset\":28},{\"endOffset\":51,\"label\":\"adjective\",\"startOffset\":46},{\"endOffset\":65,\"label\":\"adjective\",\"startOffset\":53},{\"endOffset\":74,\"label\":\"adjective\",\"startOffset\":67},{\"endOffset\":82,\"label\":\"adjective\",\"startOffset\":75},{\"endOffset\":102,\"label\":\"verb\",\"startOffset\":97},{\"endOffset\":112,\"label\":\"verb\",\"startOffset\":107},{\"endOffset\":125,\"label\":\"adjective\",\"startOffset\":113},{\"endOffset\":134,\"label\":\"adjective\",\"startOffset\":126},{\"endOffset\":143,\"label\":\"object\",\"startOffset\":135},{\"endOffset\":169,\"label\":\"adjective\",\"startOffset\":153},{\"endOffset\":176,\"label\":\"object\",\"startOffset\":170}]}}"}}]},{"datasetObjectId":"1","dataObject":{"content":"Sift 3 cups of flour into the bowl."},"annotations":[{"workerId":"private.us-west-2.0123456789","annotationData":{"content":"{\"crowd-entity-annotation\":{\"entities\":[{\"endOffset\":4,\"label\":\"verb\",\"startOffset\":0},{\"endOffset\":6,\"label\":\"number\",\"startOffset\":5},{\"endOffset\":20,\"label\":\"object\",\"startOffset\":15},{\"endOffset\":34,\"label\":\"object\",\"startOffset\":30}]}}"}}]},{"datasetObjectId":"2","dataObject":{"content":"Jen purchased 10 shares of the stock on Janurary 1st, 2020."},"annotations":[{"workerId":"private.us-west-2.0123456789","annotationData":{"content":"{\"crowd-entity-annotation\":{\"entities\":[{\"endOffset\":3,\"label\":\"person\",\"startOffset\":0},{\"endOffset\":13,\"label\":\"verb\",\"startOffset\":4},{\"endOffset\":16,\"label\":\"number\",\"startOffset\":14},{\"endOffset\":58,\"label\":\"date\",\"startOffset\":40}]}}"}}]},{"datasetObjectId":"3","dataObject":{"content":"The narrative was interesting, however the character development was weak."},"annotations":[{"workerId":"private.us-west-2.0123456789","annotationData":{"content":"{\"crowd-entity-annotation\":{\"entities\":[{\"endOffset\":29,\"label\":\"adjective\",\"startOffset\":18},{\"endOffset\":73,\"label\":\"adjective\",\"startOffset\":69}]}}"}}]}] ``` + You must use the directions in [Grant Post-Annotation Lambda Permissions to Access Annotation](sms-custom-templates-step3-lambda-permissions.md#sms-custom-templates-step3-postlambda-perms) to give your post-annotation Lambda function's execution role permission to assume the SageMaker AI execution role you use to create the labeling job. The post-annotation Lambda function uses the SageMaker AI execution role to access the annotation data file, `sample-annotations.json`, in S3. ## Test the Pre-annotation Lambda Function Use the following procedure to test the pre-annotation Lambda function created when you deployed the Ground Truth AWS Serverless Application Repository (SAR) recipe. **Test the Ground Truth SAR recipe pre-annotation Lambda function** 1. Open the [**Functions** page](https://console.aws.amazon.com/lambda/home#/functions) in the Lambda console. 1. Select the pre-annotation function that was deployed from the Ground Truth SAR recipe. The name of this function is similar to `serverlessrepo-aws-sagema-GtRecipePreHumanTaskFunc-{{}}`. 1. In the **Code source** section, select the arrow next to **Test**. 1. Select **Configure test event**. 1. Keep the **Create new test event** option selected. 1. Under **Event template**, select **SageMaker Ground Truth PreHumanTask**. 1. Give your test an **Event name**. 1. Select **Create**. 1. Select the arrow next to **Test** again and you should see that the test you created is selected, which is indicated with a dot by the event name. If it is not selected, select it. 1. Select **Test** to run the test. After you run the test, you can see the **Execution results**. In the **Function logs**, you should see a response similar to the following: ``` START RequestId: cd117d38-8365-4e1a-bffb-0dcd631a878f Version: $LATEST Received event: { "version": "2018-10-16", "labelingJobArn": "arn:aws:sagemaker:us-east-2:123456789012:labeling-job/example-job", "dataObject": { "source-ref": "s3://sagemakerexample/object_to_annotate.jpg" } } {'taskInput': {'taskObject': 's3://sagemakerexample/object_to_annotate.jpg'}, 'isHumanAnnotationRequired': 'true'} END RequestId: cd117d38-8365-4e1a-bffb-0dcd631a878f REPORT RequestId: cd117d38-8365-4e1a-bffb-0dcd631a878f Duration: 0.42 ms Billed Duration: 1 ms Memory Size: 128 MB Max Memory Used: 43 MB ``` In this response, we can see the Lambda function's output matches the required pre-annotation response syntax: ``` {'taskInput': {'taskObject': 's3://sagemakerexample/object_to_annotate.jpg'}, 'isHumanAnnotationRequired': 'true'} ``` ## Test the Post-Annotation Lambda Function Use the following procedure to test the post-annotation Lambda function created when you deployed the Ground Truth AWS Serverless Application Repository (SAR) recipe. **Test the Ground Truth SAR recipe post-annotation Lambda** 1. Open the [**Functions** page](https://console.aws.amazon.com/lambda/home#/functions) in the Lambda console. 1. Select the post-annotation function that was deployed from the Ground Truth SAR recipe. The name of this function is similar to `serverlessrepo-aws-sagema-GtRecipeAnnotationConsol-{{}}`. 1. In the **Code source** section, select the arrow next to **Test**. 1. Select **Configure test event**. 1. Keep the **Create new test event** option selected. 1. Under **Event template**, select **SageMaker Ground Truth AnnotationConsolidation**. 1. Give your test an **Event name**. 1. Modify the template code provided as follows: + Replace the Amazon Resource Name (ARN) in `roleArn` with the ARN of the SageMaker AI execution role you used to create the labeling job. + Replace the S3 URI in `s3Uri` with the URI of the `sample-annotations.json` file you added to Amazon S3. After you make these modifications, your test should look similar to the following: ``` { "version": "2018-10-16", "labelingJobArn": "arn:aws:sagemaker:us-east-2:123456789012:labeling-job/example-job", "labelAttributeName": "example-attribute", "roleArn": "arn:aws:iam::{{222222222222}}:role/{{sm-execution-role}}", "payload": { "s3Uri": "s3://{{your-bucket}}/sample-annotations.json" } } ``` 1. Select **Create**. 1. Select the arrow next to **Test** again and you should see that the test you created is selected, which is indicated with a dot by the event name. If it is not selected, select it. 1. Select the **Test** to run the test. After you run the test, you should see a `-- Consolidated Output --` section in the **Function Logs**, which contains a list of all annotations included in `sample-annotations.json`.