> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nebius.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Exporting data from Managed Service for MLflow clusters

You can export models, experiments and training runs from your Managed MLflow cluster to a directory on your machine:

1. [Configure connection to your cluster](../quickstart#configure-connection-to-mlflow-tracking) by setting the `MLFLOW_TRACKING_URI`, `MLFLOW_TRACKING_USERNAME` and `MLFLOW_TRACKING_PASSWORD` environment variables used by MLflow Tracking.

   <Note>
     Clusters with disabled public access do not have public tracking URIs. You can save the private tracking URI to `MLFLOW_TRACKING_URI` and use it to export data to a Compute virtual machine that belongs to the same [network](../../vpc/overview#network) as the cluster.
   </Note>

2. Install the [MLflow Export Import](https://github.com/mlflow/mlflow-export-import) package:

   ```bash theme={null}
   pip install git+https://github.com/mlflow/mlflow-export-import/#egg=mlflow-export-import
   ```

3. Run `export-all` to export all data from the cluster:

   ```bash theme={null}
   export-all --output-dir </output/directory> --use-threads True
   ```

   You can also use other tools from the package to export single and multiple objects, or to move them between clusters. For details, see the [tools overview](https://github.com/mlflow/mlflow-export-import/blob/master/README.md#tools-overview) and the [guide to tools for copying](https://github.com/mlflow/mlflow-export-import/blob/master/README_copy.md) in the package's documentation.