Track experiments using MLflow - Amazon SageMaker Unified Studio
MLflow Tracking ServersTracking experiments with MLflow

Amazon SageMaker Unified Studio is in preview release and is subject to change.

Track experiments using MLflow

Use MLflow in Amazon SageMaker Unified Studio to create, manage, analyze, and compare machine learning experiments.

Note

Amazon SageMaker AI MLflow dataplane API operations don't support AWS CloudTrail logs.

For more information about MLflow, see Machine learning experiments using MLflow in the Amazon SageMaker AI Developer Guide.

MLflow Tracking Servers

MLflow uses compute and storage resources provided by an MLflow Tracking Server. Each project requires an MLflow Tracking Server. Your domain administrator can configure the project defaults to automatically create the MLflow Tracking Server during project creation. Otherwise, you can create an MLflow Tracking Server on demand for the project.

When you delete a project, Amazon SageMaker Unified Studio automatically deletes the tracking server.

For more information about MLflow Tracking Servers, see MLflow Tracking Servers in the Amazon SageMaker AI Developer Guide.

For more information about project profiles for AI-ML projects, see Project profiles in the Amazon SageMaker Unified Studio Admin Guide.

Create the MLflow tracking server

After you create a project, you can create the MLflow Tracking Server for the project, if it wasn't created automatically during project creation.

To create an MLflow Tracking Server, perform the following steps:

  1. Sign in to Amazon SageMaker Unified Studio using the link that your administrator gave you.

  2. From the top banner, choose your project from the projects drop-down menu, and choose Project overview.

  3. From the left menu, choose Compute.

  4. From the tabs in the top banner, choose MLflow Tracking Servers.

  5. Choose Create MLflow Tracking Server.

  6. (Optional) Provide values to override the default values for the following fields:

    1. Name – enter a name for the server.

    2. Size – select a size for the server.

  7. Choose Create to create the server.

Edit the MLflow Tracking Server

After you create a tracking server, you can change the configured server size, if the current size isn't sufficient for the project.

To edit a tracking server, perform the following steps, starting at your project's MLflow Tracking Servers page:

  1. From the Actions drop-down menu, choose Edit. You can change the following values:

    1. Size – select a new size for the server.

    2. Artifact storage S3 path – enter a new path to the artifact storage.

  2. Choose Save changes to update the tracking server.

Start or stop an MLflow server

You can stop a running server or start a stopped server. While the tracking server is starting or stopping, it's not available for MLflow to use.

To start or stop an MLflow tracking server, perform the following steps from your project's Project details page:

  1. From the left menu, choose Compute.

  2. From the tabs in the top banner, choose MLflow Tracking Servers.

  3. From the Actions drop-down menu, choose Stop to stop a running server. Choose Start to start a stopped server.

Integrate MLflow with your environment

For information about how to integrate MLflow with your environment, see Integrate MLflow with your environment in the Amazon SageMaker AI Developer Guide.

Launching MLflow UI

You can launch MLflow Tracking Server UI from the MLflow Tracking Servers page, by performing the following steps:

  1. Navigate to the project details page for your project.

  2. From the left menu, choose Compute.

  3. From the tabs in the top banner, choose MLflow Tracking Servers.

  4. From the Actions drop-down menu, choose Open MLflow. This action uses a presigned URL to launche MLflow UI in a new tab in your current browser.

For more information, see Launch the MLflow UI using a presigned URL in the Amazon SageMaker AI Developer Guide.

Tracking experiments with MLflow

From the Experiments page, you can view and track the experiments for the current project. You can also open sample notebooks, such as for logging MLflow experiments and for registering MLflow models.

View the list of experiments

To view the list of experiments in your project, perform the following steps:

  1. Sign in to Amazon SageMaker Unified Studio using the link that your administrator gave you.

  2. From the Build drop-down menu, choose MLflow. The Experiments page displays the MLflow experiments for your project.

  3. (Optional) Enter text in the search text box to view a subset of the listed experiments.

Track an experiment

To track an MLflow experiment in your project, perform the following steps:

  1. Sign in to Amazon SageMaker Unified Studio using the link that your administrator gave you.

  2. From the Build drop-down menu, choose MLflow. The Experiments page displays the MLflow experiments for your project.

  3. From the Experiments table, choose the experiment to track. This launches a new MLflow tab in your current browser.