ML/.github/workflows/MLPipelineDevPipeline.yml

name: Deployment for Dev Model

on:
  push:
    tags:
      - "**model#dev#*"

permissions: write-all

jobs:

  Register-Prediction-Image-Dev:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3
    - name: Install packages to retrieve artifacts
      run: |
        pip install --upgrade pip
        pip install -r modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt

    - name: Retrieve artifacts (dvc.lock)
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
      run: |
        cd modules/ml-pipeline/src/pipeline
        dvc pull -r dev

    - name: Build Prediction docker image (TODO - NEED LAMBDA IMAGE, need to add version from gto registry)
      run: |
        cd modules/ml-pipeline/src/
        REGISTER_MODEL_NAME=$(echo ${{ github.event.pull_request.head.ref }} | awk -F"-" '{print $1}')
        docker build . --file Prediction.Dockerfile --tag ${REGISTER_MODEL_NAME}

    - name: ECR Login - Dev
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
      run: |
        echo "LOGIN TO ECR"

    - name: Push Prediction image to ECR - Dev
      env:
        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
      run: |
        echo "PUSH TO ECR"