AWS Lambda Model Deployment

A lambda to deploy a model via serverless after training has completed. The model artifacts will be used to create a new model, endpoint configuration and serverless endpoint configuration within SageMaker.

This repository does not create other necessarily resources within AWS, this is created via Terraform found here terraform-aws-machine-learning-pipeline. For more details on the entire flow and how this lambda is deployed, see aws-automlops-serverless-deployment.

Flowchart

The diagram below demonstrates what happens when the lambda is trigger, when a new .tar.gz object has been uploaded to the S3 Bucket.

graph LR
  S0(Start)
  T1(Create SageMaker model with output artifact)
  T2(Create Endpoint configuration using xgboost)
  T3(Create Serverless endpoint)
  T4(Send message to Model Evaluation SQS)
  E0(End)

  S0-->T1
  T1-->T2
  T2-->T3
  T3-->T4
  T4-->E0

Development

Dependencies

• Python
• Docker for Desktop
• Amazon Web Services

Usage

1. Build the docker image locally:

   docker build --no-cache -t model_deployment:local .
   

2. Run the docker image built:

   docker run --platform linux/amd64 -p 9000:8080 model_deployment:local
   

3. Send an event to the lambda via curl:

   curl "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{<REPLACE_WITH_JSON_BELOW>}'
   

   {
     "Records": [
       {
         "eventVersion": "2.0",
         "eventSource": "aws:s3",
         "awsRegion": "us-east-1",
         "eventTime": "1970-01-01T00:00:00.000Z",
         "eventName": "ObjectCreated:Put",
         "userIdentity": { "principalId": "EXAMPLE" },
         "requestParameters": { "sourceIPAddress": "127.0.0.1" },
         "responseElements": {
           "x-amz-request-id": "EXAMPLE123456789",
           "x-amz-id-2": "EXAMPLE123/5678abcdefghijklambdaisawesome/mnopqrstuvwxyzABCDEFGH"
         },
         "s3": {
           "s3SchemaVersion": "1.0",
           "configurationId": "testConfigRule",
           "bucket": {
             "name": "example-bucket",
             "ownerIdentity": { "principalId": "EXAMPLE" },
             "arn": "arn:aws:s3:::example-bucket"
           },
           "object": {
             "key": "2024-02-23/output/xgboost-2024-02-23-18-04-06-024/output/model.tar.gz",
             "size": 1024,
             "eTag": "0123456789abcdef0123456789abcdef",
             "sequencer": "0A1B2C3D4E5F678901"
           }
         }
       }
     ]
   }

GitHub Action (CI/CD)

The GitHub Action "🚀 Push Docker image to AWS ECR" will checkout the repository and push a docker image to the chosen AWS ECR using configure-aws-credentials action. The following repository secrets need to be set:

Secret	Description
AWS_REGION	The AWS Region.
AWS_ACCOUNT_ID	The AWS account ID.
AWS_ECR_REPOSITORY	The AWS ECR repository name.