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| name: CD/CD for the ml-pipeline that builds all the pipeline modules and pushes them to the private PyPI registry. From where Airflow will install the latest versions and use them in the next run. | |
| on: | |
| push: | |
| paths-ignore: | |
| - 'app-api/' | |
| - 'app-frontend/' | |
| - '**/*.yml' | |
| - '**/*.md' | |
| branches: [ "main" ] | |
| env: | |
| CLOUDSDK_CORE_PROJECT: '${{ vars.CLOUDSDK_CORE_PROJECT }}' | |
| USER: '${{ vars.USER }}' | |
| INSTANCE_NAME: '${{ vars.ML_PIPELINE_INSTANCE_NAME }}' | |
| ZONE: '${{ vars.ZONE }}' | |
| jobs: | |
| ci_cd: | |
| runs-on: ubuntu-latest | |
| steps: | |
| - uses: 'actions/checkout@v3' | |
| - id: 'auth' | |
| uses: 'google-github-actions/auth@v0' | |
| with: | |
| credentials_json: '${{ secrets.GCP_CREDENTIALS }}' | |
| - id: 'compute-ssh' | |
| uses: 'google-github-actions/ssh-compute@v0' | |
| with: | |
| project_id: '${{ env.CLOUDSDK_CORE_PROJECT }}' | |
| user: '${{ env.USER }}' | |
| instance_name: '${{ env.INSTANCE_NAME }}' | |
| zone: '${{ env.ZONE }}' | |
| ssh_private_key: '${{ secrets.GCP_SSH_PRIVATE_KEY }}' | |
| command: > | |
| cd ~/energy-forecasting && | |
| git pull && | |
| sh deploy/ml-pipeline.sh |