CI Integration¶

MVER is designed to work naturally inside CI pipelines.

Validate the Registry¶

Run mver validate in CI to catch registry inconsistencies — broken model references, missing commands, or invalid semver — before they reach production.

# GitHub Actions
- name: Validate model registry
  run: mver validate

Exits 1 on any validation failure, failing the pipeline.

Check App Declarations¶

Run mver app check in each app's CI to verify its declared group version still exists in the registry. This catches stale mver.yml files after a group version is superseded.

- name: Check model group declaration
  run: mver app check
  working-directory: apps/fraud-service

Pull Models Before Tests¶

If your tests require actual model artifacts, run mver pull as a CI step before running tests. Ensure your storage backend credentials are available as environment variables.

- name: Configure AWS credentials
  uses: aws-actions/configure-aws-credentials@v4
  with:
    aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
    aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
    aws-region: us-east-1

- name: Pull model artifacts
  run: mver pull
  working-directory: apps/fraud-service

Push After Training¶

After a model training job, register the new version and push artifacts in one pipeline:

- name: Register new model version
  run: |
    mver model version add fraud-detector ${{ env.MODEL_VERSION }} \
      --path models/fraud-detector/v${{ env.MODEL_VERSION }} \
      --created-by ci@company.com

- name: Push model artifacts
  run: mver push production@${{ env.GROUP_VERSION }}

Suggested CI Checks by Repo Location¶

Location	Recommended checks
Monorepo root / any PR	`mver validate`
Each app that uses models	`mver app check`
Model training pipeline	`mver push <group@version>` after training
App that needs artifacts for tests	`mver pull` before test step