Vectice provides an easy way to retrieve already saved information from your MLflow runs and document them into Vectice.
To log model information from MLFlow
use the following lines of code:
run_id
A unique identifier for the MLflow experiment run.
required
client
The MLflow client object used for interacting with MLflow. This parameter is mandatory to ensure compatibility and avoid issues with specific configurations.
required
url
The URL to theMLFlow
UI to access the run information.
optional
derived_from
List of datasets (or version ids) to link as lineage.
optional
You can access your MLFlow run artifacts in Vectice where you will see the model version metrics, properties, and a link to the MLFlow UI of your run.
Find this information by navigating to Models > Select model name > Select version.