# Getting started with SageMaker HyperPod using the SageMaker AI console
The following tutorial demonstrates how to create a new SageMaker HyperPod cluster and set it up with Slurm through the SageMaker AI console UI. Following the tutorial, you'll create a HyperPod cluster with three Slurm nodes, `my-controller-group`, `my-login-group`, and `worker-group-1`.
**Topics**
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## Create cluster
](#smcluster-getting-started-slurm-console-create-cluster-page)
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## Deploy resources
](#smcluster-getting-started-slurm-console-create-cluster-deploy)
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## Delete the cluster and clean resources
](#smcluster-getting-started-slurm-console-delete-cluster-and-clean)
## Create cluster
To navigate to the **SageMaker HyperPod Clusters** page and choose **Slurm** orchestration, follow these steps.
1. Open the Amazon SageMaker AI console at [https://console.aws.amazon.com/sagemaker/](https://console.aws.amazon.com/sagemaker/).
1. Choose **HyperPod Clusters** in the left navigation pane and then **Cluster Management**.
1. On the **SageMaker HyperPod Clusters** page, choose **Create HyperPod cluster**.
1. On the **Create HyperPod cluster** drop-down, choose **Orchestrated by Slurm**.
1. On the Slurm cluster creation page, you will see two options. Choose the option that best fits your needs.
1. **Quick setup** - To get started immediately with default settings, choose **Quick setup**. With this option, SageMaker AI will create new resources such as VPC, subnets, security groups, Amazon S3 bucket, IAM role, and FSx for Lustre in the process of creating your cluster.
1. **Custom setup** - To integrate with existing AWS resources or have specific networking, security, or storage requirements, choose **Custom setup**. With this option, you can choose to use the existing resources or create new ones, and you can customize the configuration that best fits your needs.
## Quick setup
On the **Quick setup** section, follow these steps to create your HyperPod cluster with Slurm orchestration.
### General settings
Specify a name for the new cluster. You can’t change the name after the cluster is created.
### Instance groups
To add an instance group, choose **Add group**. Each instance group can be configured differently, and you can create a heterogeneous cluster that consists of multiple instance groups with various instance types. To deploy a cluster, you must add at least one instance group for Controller and Compute group types.
**Important**
You can add one instance group at a time. To create multiple instance groups, repeat the process for each instance group.
Follow these steps to add an instance group.
1. For **Instance group type**, choose a type for your instance group. For this tutorial, choose **Controller (head)** for `my-controller-group`, **Login** for `my-login-group`, and **Compute (worker)** for `worker-group-1`.
1. For **Name**, specify a name for the instance group. For this tutorial, create three instance groups named `my-controller-group`, `my-login-group`, and `worker-group-1`.
1. For **Instance capacity**, choose either on-demand capacity or a training plan to reserve your compute resources.
1. For **Instance type**, choose the instance for the instance group. For this tutorial, select `ml.c5.xlarge` for `my-controller-group`, `ml.m5.4xlarge` for `my-login-group`, and `ml.trn1.32xlarge` for `worker-group-1`.
**Important**
Ensure that you choose an instance type with sufficient quotas and enough unassigned IP addresses for your account. To view or request additional quotas, see [SageMaker HyperPod quotas](sagemaker-hyperpod-prerequisites.md#sagemaker-hyperpod-prerequisites-quotas).
1. For **Instance quantity**, specify an integer not exceeding the instance quota for cluster usage. For this tutorial, enter **1** for all three groups.
1. For **Target Availability Zone**, choose the Availability Zone where your instances will be provisioned. The Availability Zone should correspond to the location of your accelerated compute capacity.
1. For **Additional storage volume per instance (GB) - optional**, specify an integer between 1 and 16384 to set the size of an additional Elastic Block Store (EBS) volume in gigabytes (GB). The EBS volume is attached to each instance of the instance group. The default mount path for the additional EBS volume is `/opt/sagemaker`. After the cluster is successfully created, you can SSH into the cluster instances (nodes) and verify if the EBS volume is mounted correctly by running the `df -h` command. Attaching an additional EBS volume provides stable, off-instance, and independently persisting storage, as described in the [Amazon EBS volumes](https://docs.aws.amazon.com/ebs/latest/userguide/ebs-volumes.html) section in the *Amazon Elastic Block Store User Guide*.
1. Choose **Add instance group**.
### Quick setup defaults
This section lists all the default settings for your cluster creation, including all the new AWS resources that will be created during the cluster creation process. Review the default settings.
## Custom setup
On the **Custom setup** section, follow these steps to create your HyperPod cluster with Slurm orchestration.
### General settings
Specify a name for the new cluster. You can’t change the name after the cluster is created.
For **Instance recovery**, choose **Automatic - *recommended*** or **None**.
### Networking
Configure your network settings for the cluster creation. These settings can't be changed after the cluster is created.
1. For **VPC**, choose your own VPC if you already have one that gives SageMaker AI access to your VPC. To create a new VPC, follow the instructions at [Create a VPC](https://docs.aws.amazon.com/vpc/latest/userguide/create-vpc.html) in the *Amazon Virtual Private Cloud User Guide*. You can leave it as **None** to use the default SageMaker AI VPC.
1. For **VPC IPv4 CIDR block**, enter the starting IP of your VPC.
1. For **Availability Zones**, choose the Availability Zones (AZ) where HyperPod will create subnets for your cluster. Choose AZs that match the location of your accelerated compute capacity.
1. For **Security groups**, create a security group or choose up to five security groups configured with rules to allow inter-resource communication within the VPC.
### Instance groups
To add an instance group, choose **Add group**. Each instance group can be configured differently, and you can create a heterogeneous cluster that consists of multiple instance groups with various instance types. To deploy a cluster, you must add at least one instance group.
**Important**
You can add one instance group at a time. To create multiple instance groups, repeat the process for each instance group.
Follow these steps to add an instance group.
1. For **Instance group type**, choose a type for your instance group. For this tutorial, choose **Controller (head)** for `my-controller-group`, **Login** for `my-login-group`, and **Compute (worker)** for `worker-group-1`.
1. For **Name**, specify a name for the instance group. For this tutorial, create three instance groups named `my-controller-group`, `my-login-group`, and `worker-group-1`.
1. For **Instance capacity**, choose either on-demand capacity or a training plan to reserve your compute resources.
1. For **Instance type**, choose the instance for the instance group. For this tutorial, select `ml.c5.xlarge` for `my-controller-group`, `ml.m5.4xlarge` for `my-login-group`, and `ml.trn1.32xlarge` for `worker-group-1`.
**Important**
Ensure that you choose an instance type with sufficient quotas and enough unassigned IP addresses for your account. To view or request additional quotas, see [SageMaker HyperPod quotas](sagemaker-hyperpod-prerequisites.md#sagemaker-hyperpod-prerequisites-quotas).
1. For **Instance quantity**, specify an integer not exceeding the instance quota for cluster usage. For this tutorial, enter **1** for all three groups.
1. For **Target Availability Zone**, choose the Availability Zone where your instances will be provisioned. The Availability Zone should correspond to the location of your accelerated compute capacity.
1. For **Additional storage volume per instance (GB) - optional**, specify an integer between 1 and 16384 to set the size of an additional Elastic Block Store (EBS) volume in gigabytes (GB). The EBS volume is attached to each instance of the instance group. The default mount path for the additional EBS volume is `/opt/sagemaker`. After the cluster is successfully created, you can SSH into the cluster instances (nodes) and verify if the EBS volume is mounted correctly by running the `df -h` command. Attaching an additional EBS volume provides stable, off-instance, and independently persisting storage, as described in the [Amazon EBS volumes](https://docs.aws.amazon.com/ebs/latest/userguide/ebs-volumes.html) section in the *Amazon Elastic Block Store User Guide*.
1. Choose **Add instance group**.
### Lifecycle scripts
You can choose to use the default lifecycle scripts or the custom lifecycle scripts, which will be stored in your Amazon S3 bucket. You can view the default lifecycle scripts in the [Awesome Distributed Training GitHub repository](https://github.com/aws-samples/awsome-distributed-training/tree/main/1.architectures/7.sagemaker-hyperpod-eks/LifecycleScripts). To learn more about the lifecycle scripts, see [Customizing SageMaker HyperPod clusters using lifecycle scripts](sagemaker-hyperpod-lifecycle-best-practices-slurm.md).
1. For **Lifecycle scripts**, choose to use default or custom lifecycle scripts.
1. For **S3 bucket for lifecycle scripts**, choose to create a new bucket or use an existing bucket to store the lifecycle scripts.
### Permissions
Choose or create an IAM role that allows HyperPod to run and access necessary AWS resources on your behalf.
### Storage
Configure the FSx for Lustre file system to be provisioned on the HyperPod cluster.
1. For **File system**, choose an existing FSx for Lustre file system, to create a new FSx for Lustre file system, or don't provision an FSx for Lustre file system.
1. For **Throughput per unit of storage**, choose the throughput that will be available per TiB of provisioned storage.
1. For **Storage capacity**, enter a capacity value in TB.
1. For **Data compression type**, choose **LZ4** to enable data compression.
1. For **Lustre version**, view the value that's recommended for the new file systems.
### Tags - optional
For **Tags - *optional***, add key and value pairs to the new cluster and manage the cluster as an AWS resource. To learn more, see [Tagging your AWS resources](https://docs.aws.amazon.com/tag-editor/latest/userguide/tagging.html).
## Deploy resources
After you complete the cluster configurations using either **Quick setup** or **Custom setup**, choose the following option to start resource provisioning and cluster creation.
+ **Submit** - SageMaker AI will start provisioning the default configuration resources and creating the cluster.
+ **Download CloudFormation template parameters** - You will download the configuration parameter JSON file and run AWS CLI command to deploy the CloudFormation stack to provision the configuration resources and creating the cluster. You can edit the downloaded parameter JSON file if needed. If you choose this option, see more instructions in [Creating SageMaker HyperPod clusters using CloudFormation templates](smcluster-getting-started-slurm-console-create-cluster-cfn.md).
## Delete the cluster and clean resources
After you have successfully tested creating a SageMaker HyperPod cluster, it continues running in the `InService` state until you delete the cluster. We recommend that you delete any clusters created using on-demand SageMaker AI instances when not in use to avoid incurring continued service charges based on on-demand pricing. In this tutorial, you have created a cluster that consists of two instance groups. One of them uses a C5 instance, so make sure you delete the cluster by following the instructions at [Delete a SageMaker HyperPod cluster](sagemaker-hyperpod-operate-slurm-console-ui.md#sagemaker-hyperpod-operate-slurm-console-ui-delete-cluster).
However, if you have created a cluster with reserved compute capacity, the status of the clusters does not affect service billing.
To clean up the lifecycle scripts from the S3 bucket used for this tutorial, go to the S3 bucket you used during cluster creation and remove the files entirely.
If you have tested running any workloads on the cluster, make sure if you have uploaded any data or if your job saved any artifacts to different S3 buckets or file system services such as Amazon FSx for Lustre and Amazon Elastic File System. To prevent any incurring charges, delete all artifacts and data from the storage or file system.