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Experiencing an exception indicating “ Yaml file exists as” referring to meta.yaml file when creating an MLflow experiment

Add mlflow.set_tracking_uri("databricks") to your code or remove the meta.yaml file referenced in the exception.

Written by alberto.umana

Last published at: May 5th, 2025

Problem

When creating an MLFlow experiment for feature engineering, you encounter an issue with the meta.yaml file. You receive an error message. 

Exception: Yaml file '/Workspace/Users/.bundle/<sample-model>/feature_engineering/features/mlruns/0/meta.yaml' exists as '/Workspace/Users/.bundle/<sample-model>/feature_engineering/features/mlruns/0/meta.yaml'

 

Cause

When MLflow is attempting to create a new experiment, it expects to write a fresh meta.yaml file. If the file already exists, especially if it contains data from a previous, incomplete, or corrupted run, MLflow raises an exception to prevent accidental overwriting. 

 

Solution

1. Use the below configuration in your code to set a tracking URI. This command properly directs MLflow to the Databricks-hosted tracking server and ensures conflicts within the local file structure are corrected.

mlflow.set_tracking_uri("databricks")

 

2. Ensure you have set DATABRICKS_HOST and DATABRICKS_TOKEN environment variables.

For more information, please refer to the “Where MLflow runs are logged” section of the Track model development using MLflow (AWS | Azure | GCP ) documentation.

 

Alternatively, remove the corrupted meta.yaml file using the following command in a notebook. 

%sh rm -rf “/Workspace/Users/.bundle/<sample-model>/feature_engineering/features/mlruns/0/meta.yaml”