Kemal Yaylali
11fb6b3d73
An end-to-end audit found the repo could not build, test or run as shipped. This fixes every finding, then adds a Cloud Run track so the demo costs about £1/month idle instead of ~£150. CI (red on its first run) - api: setuptools could not build the package (flat layout with app/ and alembic/) - web: missing @types/node; `vitest run` exited 1 with no test files - pipeline: the stub run needed a gitignored VCF, and no process had a stub block - ruff pinned, mypy configured, DB tests on real Postgres (pgserver locally, service in CI) ML serving (scores were meaningless) - the registered model now carries its own feature engineering and returns predict_proba, so serving sends raw columns and cannot drift from training - resolve by registry alias (stages are deprecated in MLflow 3) and record the real version; re-scoring upserts instead of failing on the unique constraint - ClinVar labels parsed from VEP's lowercase terms Pipeline - exact ref/alt recovered from a CHROM_POS_REF_ALT VCF ID; loading is idempotent - job status reaches running/failed/succeeded, so the UI stops polling dead jobs - DATABASE_URL travels in the environment or a Nextflow secret, never on a command line - VEP cache and plugins staged as inputs; the gcp profile runs tasks on Google Batch Deployment - the API serves /api (matching the ingress); the web app reads its API URL at runtime - migrations run in an init container under a Postgres advisory lock - terraform: custom VPC shared with Batch, private Cloud SQL, API enablement, Workload Identity bindings, Secret Manager, deletion protection - serverless track, now the default: Cloud Run services scaling to zero, a Cloud Run job for the Nextflow driver, and Neon or Cloud SQL behind one DATABASE_URL secret. GKE and Argo remain, behind -var deploy_kubernetes=true. See docs/cloud.md. Correctness and security - 409 on duplicate sample names, 422 on bad paging, natural chromosome ordering, wider VEP text columns, enum dropped on downgrade, the sample's assembly actually used - vcf_uri restricted to gs:// objects or files under the data root, blocking option injection - CORS restricted to configured origins; `make down` no longer deletes volumes Data - docs/data.md records the peer-reviewed, openly licensed sources (GIAB HG002, ClinVar, gnomAD) with citations and an honest evaluation plan; `make data` fetches a chr22 slice Verified: api 50 tests, ml 18, loader 16, web 12; ruff, mypy, svelte-check, terraform validate and both kustomize overlays clean.
48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
"""A model artifact URI (gs://...) lets the API score without an MLflow server running."""
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# Imported eagerly: mlflow loads .pyfunc lazily, so patching it by name can hit the proxy.
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import mlflow.pyfunc
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import pytest
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from app.config import settings
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from app.services import scoring
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@pytest.fixture(autouse=True)
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def clear_cache() -> None:
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scoring._models.clear()
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def no_registry(**kwargs: object) -> None:
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pytest.fail("the registry must not be contacted when MODEL_URI is set")
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def test_model_uri_skips_the_registry(monkeypatch: pytest.MonkeyPatch) -> None:
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monkeypatch.setattr(settings, "model_uri", "gs://bucket/models/pathogenicity/3")
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monkeypatch.setattr(scoring, "MlflowClient", no_registry)
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monkeypatch.setattr(mlflow.pyfunc, "load_model", lambda uri: f"model@{uri}")
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model, version = scoring.load_model()
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assert model == "model@gs://bucket/models/pathogenicity/3"
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assert version == "3"
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def test_model_uri_without_a_version_segment_still_labels_the_prediction(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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monkeypatch.setattr(settings, "model_uri", "gs://bucket/models/pathogenicity/")
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monkeypatch.setattr(scoring, "MlflowClient", no_registry)
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monkeypatch.setattr(mlflow.pyfunc, "load_model", lambda uri: "model")
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assert scoring.load_model()[1] == "pathogenicity"
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def test_model_uri_is_loaded_once(monkeypatch: pytest.MonkeyPatch) -> None:
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monkeypatch.setattr(settings, "model_uri", "gs://bucket/models/pathogenicity/3")
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monkeypatch.setattr(scoring, "MlflowClient", no_registry)
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calls: list[str] = []
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monkeypatch.setattr(mlflow.pyfunc, "load_model", lambda uri: calls.append(uri) or "model")
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scoring.load_model()
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scoring.load_model()
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assert len(calls) == 1
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