# Clean up MLflow resources
<a name="mlflow-cleanup"></a>

We recommend deleting any resources when you no longer need them. You can delete tracking servers through Amazon SageMaker Studio or using the AWS CLI. You can delete additional resources such as Amazon S3 buckets, IAM roles, and IAM policies using the AWS CLI or directly in the AWS console.

**Important**  
Don't delete the IAM role that you've used to create until you've deleted the tracking server itself. Otherwise, you'll lose access to the tracking server.

## Stop tracking servers
<a name="mlflow-cleanup-stop-server"></a>

We recommend stopping your tracking server when it is no longer in use. You can stop a tracking server in Studio or using the AWS CLI.

### Stop a tracking server using Studio
<a name="mlflow-cleanup-stop-server-ui"></a>

To stop a tracking server in Studio: 

1. Navigate to Studio.

1. Choose **MLflow** in the **Applications** pane of the Studio UI.

1. Find the tracking server of your choice in the **MLflow Tracking Servers** pane. Choose the **Stop** icon in the right corner of the tracking server pane.
**Note**  
If your tracking server is **Off**, you see the **Start** icon. If the tracking server is **On**, you see the **Stop** icon.

### Stop a tracking server using the AWS CLI
<a name="mlflow-cleanup-stop-server-cli"></a>

To stop the tracking server using the AWS CLI, use the following command: 

```
aws sagemaker stop-mlflow-tracking-server \
  --tracking-server-name $ts_name \
  --region $region
```

To start the tracking server using the AWS CLI, use the following command: 

**Note**  
It may take up to 25 minutes to start your tracking server.

```
aws sagemaker start-mlflow-tracking-server \
  --tracking-server-name $ts_name \
  --region $region
```

## Delete tracking servers
<a name="mlflow-cleanup-delete-server"></a>

You can fully delete a tracking server in Studio or using the AWS CLI. 

### Delete a tracking server using Studio
<a name="mlflow-cleanup-delete-server-ui"></a>

To delete a tracking server in Studio: 

1. Navigate to Studio.

1. Choose **MLflow** in the **Applications** pane of the Studio UI.

1. Find the tracking server of your choice in the **MLflow Tracking Servers** pane. Choose the vertical menu icon in the right corner of the tracking server pane. Then, choose **Delete**. 

1. Choose **Delete** to confirm deletion.

![\[The deletion option on a tracking server card in the MLflow Tracking Servers pane of the Studio UI.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/mlflow/mlflow-studio-delete.png)


### Delete a tracking server using the AWS CLI
<a name="mlflow-cleanup-delete-server-cli"></a>

Use the `DeleteMLflowTrackingServer` API to delete any tracking servers that you created. This may take some time.

```
aws sagemaker delete-mlflow-tracking-server \
  --tracking-server-name $ts_name \
  --region $region
```

To view the status of your tracking server, use the `DescribeMLflowTrackingServer` API and check the `TrackingServerStatus`. 

```
aws sagemaker describe-mlflow-tracking-server \
  --tracking-server-name $ts_name \
  --region $region
```

## Delete Amazon S3 buckets
<a name="mlflow-cleanup-delete-bucket"></a>

Delete any Amazon S3 bucket used as an artifact store for your tracking server using the following commands:

```
aws s3 rm s3://$bucket_name --recursive
aws s3 rb s3://$bucket_name
```

You can alternatively delete an Amazon S3 bucket associated with your tracking server directly in the AWS console. For more information, see [Deleting a bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/delete-bucket.html) in the *Amazon S3 User Guide*.

## Delete registered models
<a name="mlflow-cleanup-delete-bucket"></a>

You can delete any model groups and model versions created with MLflow directly in Studio. For more information, see [Delete a Model Group](https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-delete-model-group.html) and [Delete a Model Version](https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-delete-model-version.html).

## Delete experiments or runs
<a name="mlflow-cleanup-delete-experiments"></a>

You can use the MLflow SDK to delete experiments or runs.
+ [mlflow.delete\$1experiment](https://mlflow.org/docs/latest/python_api/mlflow.html?highlight=delete_experiment#mlflow.delete_experiment)
+ [mlflow.delete\$1run](https://mlflow.org/docs/latest/python_api/mlflow.html?highlight=delete_experiment#mlflow.delete_run)