/Secure-MLflow-Server-for-production

Primary LanguageJupyter NotebookMIT LicenseMIT

Secure MLflow server setup for production

Secret Mananger

In Secret Manager you need to configure secrets that the mlflow image will retrieve at boot time:

  • mlflow_artifact_url - path to your Cloud Storage bucket, sample value gs://mlflow
  • mlflow_database_url - SQLAlchemy-format Cloud SQL connection string (over internal GCP interfaces, not through IP), sample value postgresql+pg8000://<dbuser>:<dbpass>@/<dbname>?unix_sock=/cloudsql/dlabs:europe-west3:mlfow/.s.PGSQL.5432, the Cloud SQL instance name can be copied from Cloud SQL instance overview page
  • mlflow_tracking_username - the basic HTTP auth username for mlflow, your choice, sample value dlabs-developer
  • mlflow_tracking_password - the basic HTTP auth password for mlflow, your choice

Usage

docker build . -t mlflow

docker run --envs GCP_PROJECT=gcp-project-id -p 8080:8080 mlflow

Reference