Initialize your Kedro project
This section assume that you have installed
kedro-mlflow in your virtual environment.
Create a kedro project
This plugin must be used in an existing kedro project. If you do not have a kedro project yet, you can create it with
kedro new command. See the kedro docs for a tutorial.
If you do not have a real-world project, you can use a kedro example and follow the “Getting started” example to make a demo of this plugin out of the box.
kedro-mlflow in your kedro project
In order to use the
kedro-mlflow plugin, you need to setup its configuration and declare its hooks. Those 2 actions are detailled in the following paragraphs.
Setting up the
kedro-mlflow configuration file
kedro-mlflow>=0.11.2, the configuration file is optional. However, the plugin will use default
mlflow configuration. Specifically, the runs will be stored in a
mlruns folder at the root fo the kedro project since no
mlflow_tracking_uri is configured.
Set the working directory at the root of your kedro project:
Run the init command :
kedro mlflow init
you should see the following message:
'conf/local/mlflow.yml' successfully updated.
Note: you can create the configuration file in another kedro environment with the
kedro mlflow init --env=<other-environment>
kedro_mlflow hooks implementations must be registered with Kedro. There are 2 ways of registering hooks.
Note that you must register the hook provided by kedro-mlflow (
MlflowHook) to make the plugin work.
Declaring hooks through auto-discovery (for
kedro>=0.16.4) [Default behaviour]
If you use
kedro-mlflow hooks are auto-registered automatically by default without any action from your side. You can disable this behaviour in your
Declaring hooks statically in settings.py
If you have turned off plugin automatic registration, you can register its hooks manually by adding them to
from kedro_mlflow.framework.hooks import MlflowHook
HOOKS = (MlflowHook(),)