run_train_deploy_pipeline.yml

name: Staging Trigger Train and Deploy Pipeline
on:
  pull_request:
    types: [opened, synchronize]
    branches: [staging, main]
    paths:
      - 'train_and_deploy/**'

concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
  cancel-in-progress: true

jobs:
  run-staging-workflow:
    runs-on: ubuntu-latest
    env:
      ZENML_STORE_URL: ${{ secrets.ZENML_BENTO_PROJECTS_HOST }}
      ZENML_STORE_API_KEY: ${{ secrets.ZENML_BENTO_PROJECTS_API_KEY }}
      ZENML_STAGING_STACK : 281f82f3-6bdb-4951-bbdd-b85b57b463cc # Set this to your staging stack ID
      ZENML_GITHUB_SHA: ${{ github.event.pull_request.head.sha }}
      ZENML_GITHUB_URL_PR: ${{ github.event.pull_request._links.html.href }}
      ZENML_DEBUG: true
      ZENML_ANALYTICS_OPT_IN: false
      ZENML_LOGGING_VERBOSITY: INFO
      ZENML_DISABLE_CLIENT_SERVER_MISMATCH_WARNING: True

    steps:
      - name: Check out repository code
        uses: actions/checkout@v3

      - uses: actions/setup-python@v4
        with:
          python-version: '3.11'

      - name: Install requirements
        working-directory: ./train_and_deploy
        run: |
          pip3 install -r requirements.txt
          zenml integration install bentoml skypilot_kubernetes  s3 aws  evidently --uv -y

      - name: Connect to ZenML server
        working-directory: ./train_and_deploy
        run: |
          zenml init

      - name: Set stack (Staging)
        working-directory: ./train_and_deploy
        run: |
          zenml stack set ${{ env.ZENML_STAGING_STACK }}

      - name: Run pipeline (Staging)
        working-directory: ./train_and_deploy
        run: |
          python run.py --training