starting the experiment when the pipeline starts,
logging all the parameters, tags, metrics and artifacts under unified MLFlow run.
To make sure that the plugin discovery mechanism works, add
kedro-mlflow as a dependencies to
$ pip-compile src/requirements.in > src/requirements.txt
$ kedro install
$ kedro mlflow init
Then, adjust the kedro-mlflow configuration and point to the mlflow server by editing
conf/local/mlflow.yml and adjusting
mlflow_tracking_uri key. Then, build the image:
$ kedro docker build
And re-push the image to the remote registry.
kedro-mlflowis not installed as dependency and configuration is not in place (missing
kedro mlflow init), the MLflow experiment will not be initialized and available for pipeline tasks in Apache Airflow DAG.
Authentication to MLflow API
Given that Airflow has access to
GOOGLE_APPLICATION_CREDENTIALS variable, it’s possible to configure plugin
to use Google service account to authenticate to secured MLflow API endpoint, by generating OAuth2 token.
All is required to have
GOOGLE_APPLICATION_CREDENTIALS environment variable setup in Airflow installation and MLflow
to be protected by Google as an issuer. The other thing is to have environment variable
indicates OAuth2 audience the token should be issued for.
Also, plugin configuration requires the following:
If you store your credentials in Airflow secrets backend, e.g. HashiCorp vault, it’s possible to configure the plugin to use Airflow Variables as MLFlow API credentials.
Names of the variables need to match expected MLflow environment variable names, e.g.
You specify them in the authentiation config. For instance, setting up Basic Authentication requires the following:
params: ["MLFLOW_TRACKING_USERNAME", "MLFLOW_TRACKING_PASSWORD"]
NOTE: Authentication is an optional element and is used when starting MLflow experiment, so if MLflow is enabled in project configuration. It does not setup authentication inside Kedro nodes, this has to be handled by the project. Check GoogleOAuth2Handler class for details.