# Connecting to HyperPod clusters and submitting tasks to clusters
Cluster connection and task submission

You can launch machine learning workloads on HyperPod clusters within Amazon SageMaker Studio IDEs. When you launch Studio IDEs on a HyperPod cluster, a set of commands are available to help you get started. You can work on your training scripts, use Docker containers for the training scripts, and submit jobs to the cluster, all from within the Studio IDEs. The following section provides information on how to connect your cluster to Studio IDEs.

In Amazon SageMaker Studio you can navigate to one of your clusters in **HyperPod clusters** (under **Compute**) and view your list of clusters. You can connect your cluster to an IDE listed under **Actions**. 

You can also choose your custom file system from the list of options. For information on how to get this set up, see [Setting up HyperPod in Studio](sagemaker-hyperpod-studio-setup.md).

Alternatively, you can create a space and launch an IDE using the AWS CLI. Use the following commands to do so. The following example creates a `Private` `JupyterLab` space for `user-profile-name` with the `fs-id` FSx for Lustre file system attached.

1. Create a space using the [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/sagemaker/create-space.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/sagemaker/create-space.html) AWS CLI.

   ```
   aws sagemaker create-space \
   --region your-region \
   --ownership-settings "OwnerUserProfileName=user-profile-name" \
   --space-sharing-settings "SharingType=Private" \
   --space-settings "AppType=JupyterLab,CustomFileSystems=[{FSxLustreFileSystem={FileSystemId=fs-id}}]"
   ```

1. Create the app using the [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/sagemaker/create-app.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/sagemaker/create-app.html) AWS CLI.

   ```
   aws sagemaker create-app \
   --region your-region \
   --space-name space-name \
   --resource-spec '{"ec2InstanceType":"'"instance-type"'","appEnvironmentArn":"'"image-arn"'"}'
   ```

Once you have your applications open, you can submit tasks directly to the clusters you are connected to.