Mlflow supportΒΆ
If you use MLflow and kedro-mlflow for the Kedro pipeline runs monitoring, the plugin will automatically enable support for:
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 src/requirements.in
and run:
$ 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.