# 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: ```python # 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 the`MLFlow` 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

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