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.
51 lines
1.4 KiB
YAML
51 lines
1.4 KiB
YAML
services:
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db:
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image: postgres:16-alpine
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environment:
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POSTGRES_USER: rarelens
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POSTGRES_PASSWORD: rarelens
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POSTGRES_DB: rarelens
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ports: ["5432:5432"]
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volumes: [pgdata:/var/lib/postgresql/data]
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healthcheck:
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test: ["CMD-SHELL", "pg_isready -U rarelens"]
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interval: 5s
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retries: 10
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api:
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build: ./api
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environment:
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DATABASE_URL: postgresql+asyncpg://rarelens:rarelens@db:5432/rarelens
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MLFLOW_TRACKING_URI: http://mlflow:5000
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ports: ["8000:8000"]
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depends_on:
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db: { condition: service_healthy }
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volumes: ["./api:/app"]
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command: uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
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web:
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build:
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context: ./web
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target: build
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environment:
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PUBLIC_API_URL: http://localhost:8000/api
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ports: ["5173:5173"]
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depends_on: [api]
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volumes: ["./web:/app", "/app/node_modules"]
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command: npm run dev -- --host
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mlflow:
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image: ghcr.io/mlflow/mlflow:v3.16.0
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# --artifacts-destination makes the server proxy artifacts over HTTP; a bare local
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# --default-artifact-root would hand clients a /mlruns path only this container can see.
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command: >
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mlflow server --host 0.0.0.0
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--backend-store-uri sqlite:////mlruns/mlflow.db
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--artifacts-destination /mlruns
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ports: ["5000:5000"]
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volumes: [mlruns:/mlruns]
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volumes:
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pgdata:
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mlruns:
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