Log MLFLow runs

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:

# Use the following model wrapper to retrieve your MLFlow run artifacts
model = Model.mlflow(run_id='your_run_id', client=mlflow, url='https://your_url.com')

# Log MLFlow artifacts in Vectice
iteration.log(model)

Parameter	Description	Default
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.

MLFlow Model Version Metrics and Properties in the Vectice App

MLFlow UI URL Accessible in the Vectice App