# Quickstart: Create a SageMaker AI sandbox domain to launch Amazon EMR clusters in Studio
Quickstart: Launch Amazon EMR clusters in Studio

This section walks you through the quick set up of a complete test environment in Amazon SageMaker Studio. You will be creating a new Studio domain that lets users launch new Amazon EMR clusters directly from Studio. The steps provide an example notebook that you can connect to an Amazon EMR cluster to start running Spark workloads. Using this notebook, you will build a Retrieval Augmented Generation System (RAG) using Amazon EMR Spark distributed processing and OpenSearch vector database.

**Note**  
To get started, sign in to the AWS Management Console using an AWS Identity and Access Management (IAM) user account with admin permissions. For information on how to sign up for an AWS account and create a user with administrative access, see [Complete Amazon SageMaker AI prerequisites](gs-set-up.md).

**To set up your Studio test environment and start running Spark jobs:**
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## Step 1: Create a SageMaker AI domain for launching Amazon EMR clusters in Studio
](#studio-notebooks-emr-cluster-quickstart-setup)
+ [

## Step 2: Launch a new Amazon EMR cluster from Studio UI
](#studio-notebooks-emr-cluster-quickstart-launch)
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## Step 3: Connect a JupyterLab notebook to the Amazon EMR cluster
](#studio-notebooks-emr-cluster-quickstart-connect)
+ [

## Step 4: Clean up your CloudFormation stack
](#studio-notebooks-emr-cluster-quickstart-clean-stack)

## Step 1: Create a SageMaker AI domain for launching Amazon EMR clusters in Studio
Step 1: Create your test environment

In the following steps, you apply a CloudFormation stack to automatically create a new SageMaker AI domain. The stack also creates a user profile and configures the needed environment and permissions. The SageMaker AI domain is configured to let you directly launch Amazon EMR clusters from Studio. For this example, the Amazon EMR clusters are created in the same AWS account as SageMaker AI without authentication. You can find additional CloudFormation stacks supporting various authentication methods like Kerberos in the [getting\$1started](https://github.com/aws-samples/sagemaker-studio-emr/tree/main/cloudformation/getting_started) GitHub repository.

**Note**  
SageMaker AI allows 5 Studio domains per AWS account and AWS Region by default. Ensure your account has no more than 4 domains in your region before you create your stack.

**Follow these steps to set up a SageMaker AI domain for launching Amazon EMR clusters from Studio.**

1. Download the raw file of this [CloudFormation template](https://github.com/aws-samples/sagemaker-studio-foundation-models/blob/main/workshop-artifacts/cfn/workshop-cfn.yaml) from the `sagemaker-studio-emr` GitHub repository.

1. Go to the CloudFormation console: [https://console.aws.amazon.com/cloudformation](https://console.aws.amazon.com/cloudformation/)

1. Choose **Create stack** and select **With new resources (standard)** from the drop down menu.

1. In **Step 1**:

   1. In the **Prepare template** section, select **Choose an existing template**.

   1. In the **Specify template** section, choose **Upload a template file**.

   1. Upload the downloaded CloudFormation template and choose **Next**.

1. In **Step 2**, enter a **Stack name** and a **SageMakerDomainName** then choose **Next**.

1. In **Step 3**, keep all default values and choose **Next**.

1. In **Step 4**, check the box to acknowledge resource creation and choose **Create stack**. This creates a Studio domain in your account and region.

## Step 2: Launch a new Amazon EMR cluster from Studio UI
Step 2: Launch a new Amazon EMR cluster

In the following steps, you create a new Amazon EMR cluster from the Studio UI.

1. Go to the SageMaker AI console at [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/) and choose **Domains** in the left menu.

1. Click on your domain name **GenerativeAIDomain** to open the **Domain details** page.

1. Launch Studio from the user profile `genai-user`.

1. In the left navigation pane, go to **Data** then **Amazon EMR Clusters**.

1. On the Amazon EMR clusters page, choose **Create**. Select the template **SageMaker Studio Domain No Auth EMR** created by the CloudFormation stack and then choose **Next**.

1. Enter a name for the new Amazon EMR cluster. Optionally update other parameters such as the instance type of core and master nodes, idle timeout, or number of core nodes.

1. Choose **Create resource** to launch the new Amazon EMR cluster. 

   After creating the Amazon EMR cluster, follow the status on the **EMR Clusters** page. When the status changes to `Running/Waiting`, your Amazon EMR cluster is ready to use in Studio.

## Step 3: Connect a JupyterLab notebook to the Amazon EMR cluster
Step 3: Connect a notebook to your cluster

In the following steps, you connect a notebook in JupyterLab to your running Amazon EMR cluster. For this example, you import a notebook allowing you to build a Retrieval Augmented Generation (RAG) system using Amazon EMR Spark distributed processing and OpenSearch vector database.

1. 

**Launch JupyterLab**

   From Studio, launch the JupyterLab application.

1. 

**Create a private space**

   If you have not created a space for your JupyterLab application, choose **Create a JupyterLab space**. Enter a name for the space, and keep the space as **Private**. Leave all other settings at their default values, and then choose **Create space**. 

   Otherwise, run your JupyterLab space to launch a JupyterLab application.

1. 

**Deploy your LLM and embedding models for inference**
   + From the top menu, choose **File**, **New**, and then **Terminal**.
   + In the terminal, run the following command.

     ```
     wget --no-check-certificate https://raw.githubusercontent.com/aws-samples/sagemaker-studio-foundation-models/main/lab-00-setup/Lab_0_Warm_Up_Deploy_EmbeddingModel_Llama2_on_Nvidia.ipynb
     mkdir AWSGuides
     cd AWSGuides
     wget --no-check-certificate https://raw.githubusercontent.com/aws-samples/sagemaker-studio-foundation-models/main/lab-03-rag/AWSGuides/AmazonSageMakerDeveloperGuide.pdf
     wget --no-check-certificate https://raw.githubusercontent.com/aws-samples/sagemaker-studio-foundation-models/main/lab-03-rag/AWSGuides/EC2DeveloperGuide.pdf
     wget --no-check-certificate https://raw.githubusercontent.com/aws-samples/sagemaker-studio-foundation-models/main/lab-03-rag/AWSGuides/S3DeveloperGuide.pdf
     ```

     This retrieves the the `Lab_0_Warm_Up_Deploy_EmbeddingModel_Llama2_on_Nvidia.ipynb` notebook to your local directory and downloads three PDF files into a local `AWSGuides` folder.
   + Open `lab-00-setup/Lab_0_Warm_Up_Deploy_EmbeddingModel_Llama2_on_Nvidia.ipynb`, keep the `Python 3 (ipykernel)` kernel, and run each cell.
**Warning**  
In the **Llama 2 License Agreement** section, ensure to accept the Llama2 EULA before you continue.  
The notebook deploys two models, `Llama 2` and `all-MiniLM-L6-v2 Models`, on `ml.g5.2xlarge` for inference.

     The deployment of the models and the creation of the endpoints may take some time.

1. 

**Open your main notebook**

   In JupyterLab, open your terminal and run the following command.

   ```
   cd ..
   wget --no-check-certificate https://raw.githubusercontent.com/aws-samples/sagemaker-studio-foundation-models/main/lab-03-rag/Lab_3_RAG_on_SageMaker_Studio_using_EMR.ipynb
   ```

   You should see the additional `Lab_3_RAG_on_SageMaker_Studio_using_EMR.ipynb` notebook in the left panel of JupyterLab.

1. 

**Choose a `PySpark` kernel**

   Open your `Lab_3_RAG_on_SageMaker_Studio_using_EMR.ipynb` notebook and ensure that you are using the `SparkMagic PySpark` kernel. You can switch kernel at the top right of your notebook. Choose the current kernel name to open up a kernel selection modal, and then choose `SparkMagic PySpark`.

1. 

**Connect your notebook to the cluster**

   1. At the top right of your notebook, choose **Cluster**. This action opens a modal window that lists all of the running clusters that you have permission to access. 

   1. Select your cluster then choose **Connect**. A new credential type selection modal window opens up.

   1. Choose **No credential** and then **Connect**.  
![\[Modal showing selection of Amazon EMR credentials for JupyterLab notebooks.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/studio/emr/studio-notebooks-emr-credential-selection.png)

   1. A notebook cell automatically populates and runs. The notebook cell loads the `sagemaker_studio_analytics_extension.magics` extension, which provides functionality to connect to the Amazon EMR cluster. It then uses the `%sm_analytics` magic command to initiate the connection to your Amazon EMR cluster and the Spark application.
**Note**  
Ensure that the connection string to your Amazon EMR cluster has an authentication type set to `None`. This is illustrated by the value `--auth-type None` in the following example. You can modify the field if necessary.  

      ```
      %load_ext sagemaker_studio_analytics_extension.magics
      %sm_analytics emr connect --verify-certificate False --cluster-id your-cluster-id --auth-type None --language python
      ```

   1. Once you successfully establish the connection, your connection cell output message should display your `SparkSession` details including your cluster ID, `YARN` application ID, and a link to the Spark UI to monitor your Spark jobs.

You are ready to use the `Lab_3_RAG_on_SageMaker_Studio_using_EMR.ipynb` notebook. This example notebook runs distributed PySpark workloads for building a RAG system using LangChain and OpenSearch.

## Step 4: Clean up your CloudFormation stack
Step 4: Clean up your resources

After you are finished, make sure to terminate your two endpoints and delete your CloudFormation stack to prevent continued charges. Deleting the stack cleans up all the resources that were provisioned by the stack.

**To delete your CloudFormation stack when you are done with it**

1. Go to the CloudFormation console: [https://console.aws.amazon.com/cloudformation](https://console.aws.amazon.com/cloudformation/)

1. Select the stack you want to delete. You can search for it by name or find it in the list of stacks.

1. Click the **Delete** button to finalize deleting the stack and then **Delete** again to acknowledge that this will delete all resources created by the stack.

   Wait for the stack deletion to complete. This can take a few minutes. CloudFormation automatically cleans up all resources defined in the stack template.

1. Verify that all resources created by the stack have been deleted. For example, check for any leftover Amazon EMR cluster.

**To remove the API endpoints for a model**

1. Go to the SageMaker AI console: [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/).

1. In the left navigation pane, choose **Inference** and then **Endpoints**.

1. Select the endpoint `hf-allminil6v2-embedding-ep` and then choose **Delete** in the **Actions** drop down list. Repeat the step for the endpoint `meta-llama2-7b-chat-tg-ep`.