Connect to an EMR Serverless application from Studio - Amazon SageMaker AI

Connect to an EMR Serverless application from Studio

Data scientists and data engineers can discover and then connect to an EMR Serverless application directly from the Studio user interface. Before you begin, ensure that you have created an EMR Serverless application by following the instructions in Create EMR Serverless applications from Studio.

You can connect an EMR Serverless application to a new JupyterLab notebook directly from the Studio UI, or choose to initiate the connection in a notebook of a running JupyterLab application.

Important

When using Studio, you can only discover and connect to EMR Serverless applications for JupyterLab applications that are launched from private spaces. Ensure that the EMR Serverless applications are located in the same AWS region as your Studio environment. Your JupyterLab space must use a SageMaker Distribution image version 1.10 or higher.

To connect an EMR Serverless application to a new JupyterLab notebook from the Studio UI:

  1. In the Studio UI, navigate to the left-side panel and select the Data node in the left navigation menu. Then, scroll and choose the Amazon EMR applications and clusters option. This opens up a page that displays the Amazon EMR applications that you can access from within the Studio environment, under the Serverless applications tab.

    Note

    If you or your administrator have configured the permissions to allow cross-account access to EMR Serverless applications, you can view a consolidated list of applications across all accounts that you have granted access to Studio.

  2. Select an EMR Serverless application you want to connect to a new notebook, and then choose Attach to notebook. This opens up a modal window displaying the list of your JupyterLab spaces.

    • Select the private space from which you want to launch a JupyterLab application, and then choose Open notebook. This launches a JupyterLab application from your chosen space and opens a new notebook.

    • Alternatively, you can create a new private space by choosing the Create new space button at the top of the modal window. Enter a name for your space and then choose Create space and open notebook. This creates a private space with the default instance type and latest SageMaker distribution image available, launches a JupyterLab application, and opens a new notebook.

  4. Choose the name of the IAM runtime execution role that your EMR Serverless application can assume for the job run. Upon selection, a connection command populates the first cell of your notebook and initiates the connection with the EMR Serverless application.

    Important

    To successfully connect a JupyterLab notebook to an EMR Serverless application, you must first associate the list of runtime roles with your domain or user profile, as outlined in Set up the permissions to enable listing and launching Amazon EMR applications from SageMaker Studio. Failing to complete this step will prevent you from establishing the connection.

    Once the connection succeeds, a message confirms the connection, starts your EMR Serverless application, and initiates your Spark session.

    Note

    When you connect to an EMR Serverless application, its status transitions from either Stopped or Created to Started.

Alternatively, you can connect to a cluster from a JupyterLab notebook.

  1. Choose the Cluster button at the top right of your notebook. This opens a modal window listing the EMR Serverless applications that you can access. You can see the applications in the Serverless applications tab.

  2. Select the application to which you want to connect, then choose Connect.

  3. EMR Serverless supports runtime IAM roles that were preloaded when setting the required permissions as outlined in Set up the permissions to enable listing and launching Amazon EMR applications from SageMaker Studio. Failing to complete this step will prevent you from establishing the connection.

    You can select your role from the Amazon EMR execution role drop down menu. When you connect to an EMR Serverless, Studio adds a code block to an active cell of your notebook to establish the connection.

  4. An active cell populates and runs. This cell contains the connection magic command to connect your notebook to your application.

    Once the connection succeeds, a message confirms the connection and the start of the Spark application. You can begin submitting your data processing jobs to your EMR Serverless application.