# Test pre-annotation and post-annotation Lambda functions 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.amazonaws.cn/lambda/latest/dg/getting-started-create-function.html#gettingstarted-zip-function) tutorial with the console in the Amazon 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 Amazon 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 Amazon 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.amazonaws.cn/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 Amazon Serverless Application Repository (SAR) recipe. **Test the Ground Truth SAR recipe pre-annotation Lambda function** 1. Open the [**Functions** page](https://console.amazonaws.cn/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 Amazon Serverless Application Repository (SAR) recipe. **Test the Ground Truth SAR recipe post-annotation Lambda** 1. Open the [**Functions** page](https://console.amazonaws.cn/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`.