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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' | |
# The machine running the ML pipeline is stopped due to high costs. | |
# Uncomment the line below if you want to automatically start the CI/CD for the ML pipeline. | |
# branches: [ "main" ] | |
branches: [] | |
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 |