# Tutorial: Use a SageMaker AI notebook with your development endpoint
<a name="dev-endpoint-tutorial-sage"></a>

 In AWS Glue, you can create a development endpoint and then create a SageMaker AI notebook to help develop your ETL and machine learning scripts. A SageMaker AI notebook is a fully managed machine learning compute instance running the Jupyter Notebook application.

1. In the AWS Glue console, choose **Dev endpoints** to navigate to the development endpoints list. 

1. Select the check box next to the name of a development endpoint that you want to use, and on the **Action** menu, choose **Create SageMaker notebook**.

1. Fill out the **Create and configure a notebook** page as follows:

   1. Enter a notebook name.

   1. Under **Attach to development endpoint**, verify the development endpoint.

   1. Create or choose an AWS Identity and Access Management (IAM) role.

      Creating a role is recommended. If you use an existing role, ensure that it has the required permissions. For more information, see [Step 6: Create an IAM policy for SageMaker AI notebooks](create-sagemaker-notebook-policy.md).

   1. (Optional) Choose a VPC, a subnet, and one or more security groups.

   1. (Optional) Choose an AWS Key Management Service encryption key.

   1. (Optional) Add tags for the notebook instance.

1. Choose **Create notebook**. On the **Notebooks** page, choose the refresh icon at the upper right, and continue until the **Status** shows `Ready`.

1. Select the check box next to the new notebook name, and then choose **Open notebook**.

1. Create a new notebook: On the **jupyter** page, choose **New**, and then choose **Sparkmagic (PySpark)**.

   Your screen should now look like the following:  
![\[The jupyter page has a menu bar, toolbar, and a wide text field into which you can enter statements.\]](http://docs.aws.amazon.com/glue/latest/dg/images/sagemaker-notebook.png)

1. (Optional) At the top of the page, choose **Untitled**, and give the notebook a name.

1. To start a Spark application, enter the following command into the notebook, and then in the toolbar, choose **Run**.

   ```
   spark
   ```

   After a short delay, you should see the following response:  
![\[The system response shows Spark application status and outputs the following message: SparkSession available as 'spark'.\]](http://docs.aws.amazon.com/glue/latest/dg/images/spark-command-response.png)

1. Create a dynamic frame and run a query against it: Copy, paste, and run the following code, which outputs the count and schema of the `persons_json` table.

   ```
   import sys
   from pyspark.context import SparkContext
   from awsglue.context import GlueContext
   from awsglue.transforms import *
   glueContext = GlueContext(SparkContext.getOrCreate())
   persons_DyF = glueContext.create_dynamic_frame.from_catalog(database="legislators", table_name="persons_json")
   print ("Count:  ", persons_DyF.count())
   persons_DyF.printSchema()
   ```