Clean up MLflow resources - Amazon SageMaker
Stop tracking serversDelete tracking serversDelete Amazon S3 bucketsDelete registered modelsDelete experiments or runs
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Clean up MLflow resources

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

Stop tracking servers

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

Stop a tracking server using Studio

To stop a tracking server in Studio:

  1. Navigate to Studio.

  2. Choose MLflow in the Applications pane of the Studio UI.

  3. 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 Amazon CLI

Use the StopMLflowTrackingServer API to delete any tracking servers that you created. For more information, see Stop or start the MLflow Tracking Server.

Delete tracking servers

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

Delete a tracking server using Studio

To delete a tracking server in Studio:

  1. Navigate to Studio.

  2. Choose MLflow in the Applications pane of the Studio UI.

  3. 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.

  4. Choose Delete to confirm deletion.

The deletion option on a tracking server card in the MLflow Tracking Servers pane of the Studio UI.

Delete a tracking server using the Amazon CLI

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

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 Amazon console. For more information, see Deleting a bucket in the Amazon S3 User Guide.

Delete registered models

You can delete any model groups and model versions created with MLflow directly in Studio. For more information, see Delete a Model Group and Delete a Model Version.

Delete experiments or runs

You can use the MLflow SDK to delete experiments or runs.