# Track experiments using MLflow Track experiments using MLflow Amazon SageMaker Unified Studio supports two options for tracking experiments with MLflow: MLflow Apps and MLflow Tracking Servers. MLflow Apps are the latest offering with faster startup times and cross-account sharing, while MLflow Tracking Servers provide traditional MLflow functionality. For more information about MLflow, see [Machine learning experiments using MLflow](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow.html) in the *Amazon SageMaker AI Developer Guide*. **Topics** + [ ## Use MLflow Apps for experiment tracking ](#sagemaker-experiments-mlflow-apps) + [ ## Use MLflow Tracking Servers for experiment tracking ](#sagemaker-experiments-tracking-servers) ## Use MLflow Apps for experiment tracking Use MLflow Apps in Amazon SageMaker Unified Studio to track, manage, analyze, and compare machine learning experiments. MLflow Apps are the latest managed MLflow offering and provide faster startup times, cross-account sharing, and integration with other SageMaker AI features. MLflow Apps should be preferred over existing MLflow Tracking Servers. MLflow Apps provide capabilities for experiment tracking, model registry, and tracing generative AI applications. Each MLflow App includes compute resources, backend metadata storage, and artifact storage in Amazon S3. **Note** MLflow Apps are different from MLflow Tracking Servers. MLflow Apps offer additional features such as faster startup time, cross-account sharing, and automatic model registration. For information about MLflow Tracking Servers, see [Use MLflow Tracking Servers for experiment tracking](#sagemaker-experiments-tracking-servers). For more information about MLflow Apps, see [MLflow App Setup](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-app-setup.html) in the *Amazon SageMaker AI Developer Guide*. **Topics** + [ ### MLflow Apps overview ](#sagemaker-experiments-mlflow-apps-overview) + [ ### Prerequisites ](#sagemaker-experiments-mlflow-apps-prerequisites) + [ ### Create an MLflow App ](#sagemaker-experiments-mlflow-apps-create) + [ ### Edit an MLflow App ](#sagemaker-experiments-mlflow-apps-edit) + [ ### Delete an MLflow App ](#sagemaker-experiments-mlflow-apps-delete) + [ ### Launch the MLflow UI ](#sagemaker-experiments-mlflow-apps-launch-ui) + [ ### Integrate MLflow with your environment ](#sagemaker-experiments-mlflow-apps-integrate) ### MLflow Apps overview An MLflow App is a stand-alone HTTP server that serves multiple REST API endpoints for tracking runs and experiments. MLflow Apps provide the following capabilities: + Experiment tracking: Track parameters, metrics, and artifacts across multiple training runs to identify the best performing models. + Model registry: Manage model versions and catalog models for production deployment. + Tracing: Record inputs, outputs, and metadata at every step of a generative AI application to identify issues and maintain traceability. + Automatic model registration: Automatically register models from MLflow Model Registry to SageMaker AI Model Registry. You can create MLflow Apps for your project. Your domain administrator can configure the project profile to automatically create an MLflow App during project creation, though this is not recommended due to default quota limits and increased project creation latency. We recommend creating MLflow Apps on demand after project creation. When you delete a project, Amazon SageMaker Unified Studio automatically deletes associated MLflow Apps. ### Prerequisites Before you create an MLflow App, ensure you have the following: + An Amazon S3 bucket in the same AWS Region as your project for artifact storage. The MLflow App uses this bucket to store model artifacts, images, and data files. + Appropriate IAM permissions to create and manage MLflow Apps. Your domain administrator configures these permissions through the following IAM policies: + SageMakerStudioProjectRoleMachineLearningPolicy + SageMakerStudioProjectProvisioningRolePolicy + SageMakerStudioUserIAMDefaultExecutionPolicy + SageMakerStudioAdminIAMDefaultExecutionPolicy For more information about IAM permissions, see the Security chapter. ### Create an MLflow App After you create a project, you can create an MLflow App for the project if it was not created automatically during project creation. To create an MLflow App, perform the following steps: 1. Sign in to Amazon SageMaker Unified Studio using the link that your administrator gave you. 1. From the left menu, choose **Compute**. 1. From the tabs in the top banner, choose **MLflow**. 1. Choose **Create MLflow App**. 1. For **Name**, enter a name for the MLflow App. The name must start with a letter or number and can contain letters, numbers, and hyphens. 1. Choose **Create** to create the MLflow App. **Note** It may take 2-3 minutes to complete MLflow App creation. When you successfully create an MLflow App, it automatically starts. ### Edit an MLflow App After you create an MLflow App, you can change the artifact storage location. To edit an MLflow App, perform the following steps: 1. From the left menu, choose **Compute**. 1. From the tabs in the top banner, choose **MLflow**. 1. From the **Actions** drop-down menu, choose **Edit**. 1. For **Artifact storage S3 path**, enter a new path to the artifact storage. 1. Choose **Save changes** to update the MLflow App. ### Delete an MLflow App You can delete an MLflow App when you no longer need it. Deleting an MLflow App removes the compute resources but does not delete the artifacts stored in Amazon S3. To delete an MLflow App, perform the following steps: 1. From the left menu, choose **Compute**. 1. From the tabs in the top banner, choose **MLflow**. 1. From the **Actions** drop-down menu, choose **Delete**. 1. In the confirmation dialog, enter the MLflow App name to confirm deletion. **Note** An MLflow App is not available for use while it is in certain states, such as creating, pending deletion, or other transitional states. + Choose **Delete** to delete the MLflow App. **Important** Deleting an MLflow App is permanent and cannot be undone. Ensure you have backed up any important experiment data before deleting the MLflow App. ### Launch the MLflow UI You can launch the MLflow UI to view and manage your experiments, models, and traces. To launch the MLflow UI, perform the following steps: 1. From the left menu, choose **Compute**. 1. From the tabs in the top banner, choose **MLflow**. 1. Choose the **Open** button next to the MLflow App. This action uses a presigned URL to launch the MLflow UI in a new tab in your current browser. For more information about using the MLflow UI, see [Launch the MLflow UI using a presigned URL](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-launch-ui.html) in the *Amazon SageMaker AI Developer Guide*. ### Integrate MLflow with your environment After you create an MLflow App, you can integrate it with your development environment to track experiments and log metrics. To integrate MLflow with your environment, you need the MLflow App ARN. You can find the ARN on the MLflow App details page. For detailed information about integrating MLflow with your environment, including code examples for Python notebooks, see [Integrate MLflow with your environment](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-track-experiments.html) in the *Amazon SageMaker AI Developer Guide*. ## Use MLflow Tracking Servers for experiment tracking Use MLflow Tracking Servers in Amazon SageMaker Unified Studio to track, manage, analyze, and compare machine learning experiments. MLflow Tracking Servers provide compute and storage resources for experiment tracking. Each project can have 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. **Note** MLflow Tracking Servers are different from MLflow Apps. MLflow Apps offer additional features such as faster startup time, cross-account sharing, and automatic model registration. For information about MLflow Apps, see [Use MLflow Apps for experiment tracking](#sagemaker-experiments-mlflow-apps). 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](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server.html) in the *Amazon SageMaker AI Developer Guide*. For more information about project profiles for AI-ML projects, see [Project profiles](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/project-profiles.html) in the *Amazon SageMaker Unified Studio Admin Guide*. ### Create an MLflow Tracking Server After you create a project, you can create an 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. 1. From the top banner, choose your project from the projects drop-down menu, and choose **Project overview**. 1. From the left menu, choose **Compute**. 1. From the tabs in the top banner, choose **MLflow**. 1. Choose **Create MLflow Tracking Server**. 1. (Optional) Provide values to override the default values for the following fields: 1. Name – enter a name for the server. 1. Size – select a size for the server. 1. Choose **Create** to create the server. ### Edit an 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 from your project's **MLflow** tab under **Compute**: 1. From the **Actions** drop-down menu, choose **Edit**. You can change the following values: 1. Size – select a new size for the server. 1. Artifact storage S3 path – enter a new path to the artifact storage. 1. Choose **Save changes** to update the tracking server. ### Start or stop an MLflow Tracking 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**. 1. From the tabs in the top banner, choose **MLflow**. 1. 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](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-track-experiments.html) in the *Amazon SageMaker AI Developer Guide*. ### Launch the MLflow UI You can launch the MLflow Tracking Server UI from the **MLflow** tab under **Compute**, by performing the following steps: 1. Navigate to the project details page for your project. 1. From the left menu, choose **Compute**. 1. From the tabs in the top banner, choose **MLflow**. 1. From the **Actions** drop-down menu, choose **Open MLflow**. This action uses a presigned URL to launch the MLflow UI in a new tab in your current browser. For more information, see [Launch the MLflow UI using a presigned URL](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-launch-ui.html) in the *Amazon SageMaker AI Developer Guide*.