Steps to deploy the histogram preprocessing script in the AWS lambda

It is supposed that the user has some prior knowledge about lambda and docker.

Pre-requisites

• (Install and) Configure Docker
• Install Terraform
• Install AWS CLI
• Setup AWS credentials
  • aws configure Preferred. This deployment will assume that aws configure is used.
  • Need aws_access_key_id and aws_secret_access_key key values; inside ~/.aws/credentials

Deployement Details

1. Export env variables for keys mentioned in .env.example into shell session.
   • example: export TF_VAR_aws_creds_path="xxxxxxx" TF_VAR_aws_region="xxxxxxx" TF_VAR_accountId="xxxxxxx"
2. Deploy using bash deploy.sh. The bash script does the following things:
   1. Deploy the script to ECR using the predeploy script. - bash predeploy.sh - This will create a repository in the AWS ECR and - Build and upload the docker image to the ECR.
   2. Uses the following terraform commands to build and deploy the infrastrucutre needed for Histogram Preprocessing Tool:
      • terraform init
      • terraform plan
      • terraform apply
        • This will create a new lambda function (using docker inn ECR)
        • Configure REST API gateway for the lambda.
        • Add appropriate roles and permissions.
        • Add cloudwatch logs for the API and Lambda.
        • And add necessary configuration for the lambda execution environment.
3. Use the Histogram_preprocessed_data.postman_collection.json postman collection to test the Lambda.

After terraform finishes building the Histogram tool infrastructure, it outputs env varaibles that can be used in the frontend.

• For <sensitive> as output value, use terraform output <key_name>

To remove the Histogram preprocessing Tool

 - `terraform destroy`
      - removes the Lambda function, API Gateway and permissions.
 - Goto AWS ECR Management console and remove the created ECR.