xgboost-built-in-algo-train-in-sagemaker-deploy-with-lambda

Train XGBoost built-in algorithm in SageMaker, inference with AWS Lambda

This examples illustrates how to target Direct Marketing with Amazon SageMaker XGBoost built-in algorithm. You train the model using SageMaker and inference with AWS Lambda.

You'll first train a SageMaker XGBoost built-in algorithm using the IPython notebook in SageMaker Notebook instance. And then download the model to your computer, and build and deploy the Lambda function to perform the inference.

This project contains source code and supporting files for a serverless application that you can deploy with the SAM CLI. It includes the following files and folders.

• notebooks - IPython notebook with the code to train a SageMaker XGBoost built-in algorithm.
• app - Code for the application's Lambda function.
• events - Invocation events that you can use to invoke the function.
• template.yaml - A template that defines the application's AWS resources.

The application uses several AWS resources, including Lambda functions. These resources are defined in the template.yaml file in this project. You can update the template to add AWS resources through the same deployment process that updates your application code.

Train a SageMaker XGBoost built-in algorithm

You'll be running the Targeting Direct Marketing with Amazon SageMaker XGBoost, Inference with AWS Lambda notebook to train a SageMaker XGBoost built-in algorithm.

You can run this notebook in SageMaker Notebook instance

This notebooks is identical to the original Targeting Direct Marketing with Amazon SageMaker XGBoost notebook, except the fact that you'll deploy the model in Lambda function.

Copy XGBoost model file and test data locations in S3

Copy the XGBoost model and test data location in S3. This is required in order to be able to download the model to your computer, for the Lambda function to use, and to configure the Lambda test event.

Copy the model and test data locations in S3 from the last two cells in the notebook

Download XGBoost model file to your computer

Using AWS CLI, copy the XGBoost model file to model folder on your computer.

xgboost-built-in-algo-train-in-sagemaker-deploy-with-lambda$ aws s3 cp <MODEL_LOCATION_ON_S3> ./model/

Example:

xgboost-built-in-algo-train-in-sagemaker-deploy-with-lambda$ aws s3 cp s3://sagemaker-us-east-1-1234567890123/sagemaker/DEMO-xgboost-dm-lambda-inference/output/sagemaker-xgboost-2021-02-04-08-35-14-514/output/model.tar.gz ./model/

Prepare test event for the Lambda

Update bucket and prefix values in Lambda test event with the ones you copied form the IPyhton notebook.

This is the location of the test csv file for the Lambda function to download from S3 and perform inference.

{
  "bucket":"<REPLACE_WITH_YOUR_BUCKET_FROM_IPYTHON_NOTEBOOK>",
  "prefix":"<REPLACE_WITH_YOUR_PREFIX_FROM_IPYTHON_NOTEBOOK>",
  "file": "test.csv"
}

Example:

{
  "bucket":"sagemaker-us-east-1-1234567890123",
  "prefix":"sagemaker/DEMO-xgboost-dm-lambda-inference/test/",
  "file": "test.csv"
}

Deploy the sample application

The Serverless Application Model Command Line Interface (SAM CLI) is an extension of the AWS CLI that adds functionality for building and testing Lambda applications. It uses Docker to run your functions in an Amazon Linux environment that matches Lambda. It can also emulate your application's build environment and API.

To use the SAM CLI, you need the following tools.

• SAM CLI - Install the SAM CLI
• Docker - Install Docker community edition

You may need the following for local testing.

• Python 3 installed

To build and deploy your application for the first time, run the following in your shell:

sam build
sam deploy --guided

The first command will build a docker image from a Dockerfile and then copy the source of your application inside the Docker image. The second command will package and deploy your application to AWS, with a series of prompts:

• Stack Name: The name of the stack to deploy to CloudFormation. This should be unique to your account and region, and a good starting point would be something matching your project name.
• AWS Region: The AWS region you want to deploy your app to.
• Confirm changes before deploy: If set to yes, any change sets will be shown to you before execution for manual review. If set to no, the AWS SAM CLI will automatically deploy application changes.
• Allow SAM CLI IAM role creation: Many AWS SAM templates, including this example, create AWS IAM roles required for the AWS Lambda function(s) included to access AWS services. By default, these are scoped down to minimum required permissions. To deploy an AWS CloudFormation stack which creates or modified IAM roles, the CAPABILITY_IAM value for capabilities must be provided. If permission isn't provided through this prompt, to deploy this example you must explicitly pass --capabilities CAPABILITY_IAM to the sam deploy command.
• Save arguments to samconfig.toml: If set to yes, your choices will be saved to a configuration file inside the project, so that in the future you can just re-run sam deploy without parameters to deploy changes to your application.

Use the SAM CLI to build and test locally

Build your application with the sam build command.

xgboost-built-in-algo-train-in-sagemaker-deploy-with-lambda$ sam build

The SAM CLI builds a docker image from a Dockerfile and then installs dependencies defined in requirements.txt inside the docker image. The processed template file is saved in the .aws-sam/build folder.

Test a single function by invoking it directly with a test event. An event is a JSON document that represents the input that the function receives from the event source. Test events are included in the events folder in this project.

Run functions locally and invoke them with the sam local invoke command.

xgboost-built-in-algo-train-in-sagemaker-deploy-with-lambda$ sam local invoke XGBoostDMInferenceFunction --event events/event.json

Testing your Lambda function in the Cloud

1. In the Lambda Console, select Configure test events from the Test events dropdown.
2. For Event Name, enter InferenceTestEvent.
3. Copy the event JSON from here and paste in the dialog box.
4. Choose Create.

After saving, you see InferenceTestEvent in the Test list. Now choose Test.

You see the Lambda function inference result, log output, and duration:

Fetch, tail, and filter Lambda function logs

To simplify troubleshooting, SAM CLI has a command called sam logs. sam logs lets you fetch logs generated by your deployed Lambda function from the command line. In addition to printing the logs on the terminal, this command has several nifty features to help you quickly find the bug.

NOTE: This command works for all AWS Lambda functions; not just the ones you deploy using SAM.

xgboost-built-in-algo-train-in-sagemaker-deploy-with-lambda$ sam logs -n XGBoostDMInferenceFunction --stack-name xgboost-built-in-algo-train-in-sagemaker-deploy-with-lambda --tail

You can find more information and examples about filtering Lambda function logs in the SAM CLI Documentation.

Cleanup

To delete the sample application that you created, use the AWS CLI. Assuming you used your project name for the stack name, you can run the following:

aws cloudformation delete-stack --stack-name xgboost-built-in-algo-train-in-sagemaker-deploy-with-lambda

Resources

See the AWS SAM developer guide for an introduction to SAM specification, the SAM CLI, and serverless application concepts.