Deploy Classification Model pipeline with Terraform using Amazon SageMaker

This repository contains Infrastructure as Code (IaC) to create and manage AWS infrastructure for classification pipeline that predicts loan eligibility of customers with SageMaker and Step Functions.

Deploy AWS Infrastructure with Terraform

In order to deploy the ML pipeline, you will need to adjust the project name variable. The code for the Terraform part is in this repository in the folder:

/terraform

When initialising for the first time:

Follow the steps below to deploy the infrastructure with Terraform.

export AWS_PROFILE=<your_aws_cli_profile_name>

cd terraform/infrastructure

terraform init

terraform plan

terraform apply

Check the output and make sure the planned resources appear correctly and confirm with ‘yes’ in the apply stage if everything is correct. Once successfully applied, go to ECR (or check the output of Terraform in the Terminal) and get the URL for your ECR repository just created via Terraform.

Deploy Docker Image to ECR using GitHub Actions

For the ML pipeline and SageMaker to train and provision an endpoint for inference, you need to provide a Docker image and store it in ECR.

Store AWS credentials in GitHub Secrets

In your GitHub repository, go to Settings > Secrets and add the AWS access key ID and secret access key as secrets.

Set up GitHub Actions workflow

Make sure that the AWS_REGION and ECR_REPOSITORY environment variables in the .github/workflow/aws.yaml file are compatible with the terraform configuration. You can also get the ECR repository name through the AWS console.

Commit and push changes

Commit the workflow file and any other changes you've made, then push them to your GitHub repository.

Monitor the workflow

Go to the "Actions" tab in your GitHub repository to monitor the progress of the workflow. If everything is set up correctly, it should build your Docker image and push it to your AWS ECR repository whenever changes are pushed to the specified branch.

Check the ECR repository

After the build and deployment process is successful, you can check your ECR repository via the AWS console. An image tagged with "latest" will be created under the repository.

Run the ML pipeline

In order to train and run the ML pipeline, go to Step Functions and start the execution. You can check progress of SageMaker also in the Training Jobs section of SageMaker and once the SageMaker Endpoint is created you can also check your SageMaker Endpoint. After running the State Machine in Step Functions successfully, you will see the SageMaker Endpoint being created in the AWS Console in the SageMaker Endpoints section. Make sure to wait for the Status to change to “InService”.

Invoke your endpoint

In order to invoke your endpoint, you can use the following python script with Amazon SageMaker notebook.

import boto3
from io import StringIO
import pandas as pd

client = boto3.client('sagemaker-runtime')

endpoint_name = 'Your endpoint name' # Your endpoint name.
content_type = "text/csv"   # The MIME type of the input data in the request body.

payload = pd.DataFrame([["LP001005", "Female", "Yes", 0, "Graduate", "Yes", 30000, 100, 66.0, 36.0, 1.0, "Urban"]])
csv_file = StringIO()
payload.to_csv(csv_file, sep=",", header=False, index=False)
payload_as_csv = csv_file.getvalue()

response = client.invoke_endpoint(
    EndpointName=endpoint_name, 
    ContentType=content_type,
    Body=payload_as_csv
    )

label = response['Body'].read().decode('utf-8')
print(label)

Cleanup

In order to clean up, you can destroy the infrastructure created by Terraform with the command “terraform destroy”. But you will need to delete the data and files in the S3 buckets first. Further, the SageMaker Endpoint (or multiple SageMaker Endpoints if run multiple times) created via Step Functions is not managed via Terraform, but rather deployed when running the ML pipeline with Step Functions. Therefore, make sure you delete the SageMaker Endpoints created via the Step Function ML pipeline as well to avoid unnecessary costs.

Steps:

Delete the dataset in the S3 training bucket and all models you trained via the ML pipeline in the S3 bucket for the models in the AWS Console or via the AWS CLI
Destroy the infrastructure created via Terraform

cd terraform/infrastructure

terraform destroy

Delete the SageMaker Endpoints, Endpoint Configuration and Models created via the Step Function in the AWS Console or via the AWS CLI.

aParsecFromFuture / AWS_custom_container