Installing the training operator

See the following sections to learn about how to install the training operator.

Prerequisites

Before you use the HyperPod training operator, you must have completed the following prerequisites:

Installing the training operator

You can now install the HyperPod training operator through the SageMaker AI console, the Amazon EKS console, or with the AWS CLI The console methods offer simplified experiences that help you install the operator. The AWS CLI offers a programmatic approach that lets you customize more of your installation.

Between the two console experiences, SageMaker AI provides a one-click installation creates the IAM execution role, creates the pod identity association, and installs the operator. The Amazon EKS console installation is similar, but this method doesn't automatically create the IAM execution role. During this process, you can choose to create a new IAM execution role with information that the console pre-populates. By default, these created roles only have access to the current cluster that you're installing the operator in. Unless you edit the role's permissions to include other clusters, if you remove and reinstall the operator, you must create a new role.

SageMaker AI console (recommended)

1. Open the Amazon SageMaker AI console at https://console.aws.amazon.com/sagemaker/.

2. Go to your cluster's details page.

3. On the Dashboard tab, locate the add-on named Amazon SageMaker HyperPod training operator, and choose install. During the installation process, SageMaker AI creates an IAM execution role with permissions similar to the AmazonSageMakerHyperPodTrainingOperatorAccess managed policy and creates a pod identity association between your Amazon EKS cluster and your new execution role.

Amazon EKS console

Note

If you install the add-on through the Amazon EKS cluster, first make sure that you've tagged your HyperPod cluster with the key-value pair SageMaker:true. Otherwise, the installation will fail.

1. Open the Amazon EKS console at https://console.aws.amazon.com/eks/home#/clusters.

2. Go to your EKS cluster, choose Add-ons, then choose Get more Add-ons.

3. Choose Amazon SageMaker HyperPod training operator, then choose Next.

4. Under Version, the console defaults to the latest version, which we recommend that you use.

5. Under Add-on access, choose a pod identity IAM role to use with the training operator add-on. If you don't already have a role, choose Create recommended role to create one.

6. During this role creation process, the IAM console pre-populates all of the necessary information, such as the use case, the AmazonSageMakerHyperPodTrainingOperatorAccess managed policy and other required permissions, the role name, and the description. As you go through the steps, review the information, and choose Create role.

7. In the EKS console, review your add-on's settings, and then choose Create.

CLI

1. Make sure that the IAM execution role for your HyperPod cluster has a trust relationship that allows EKS Pod Identity to assume the role or or create a new IAM role with the following trust policy. Alternatively, you could use the Amazon EKS console to install the add-on, which creates a recommended role.

   {
     "Version": "2012-10-17",
     "Statement": [
       {
         "Sid": "AllowEksAuthToAssumeRoleForPodIdentity",
         "Effect": "Allow",
         "Principal": {
           "Service": "pods.eks.amazonaws.com"
         },
         "Action": [
           "sts:AssumeRole",
           "sts:TagSession",
           "eks-auth:AssumeRoleForPodIdentity"
         ]
       }
     ]
   }

2. Attach the AmazonSageMakerHyperPodTrainingOperatorAccess managed policy to your created role.

3. Then create a pod identity association between your EKS cluster, your IAM role, and your new IAM role.

   aws eks create-pod-identity-association \
   --cluster-name my-eks-cluster \
   --role-arn ARN of your execution role \
   --namespace aws-hyperpod \
   --service-account hp-training-operator-controller-manager \
   --region AWS Region

4. After you finish the process, you can use the ListPodIdentityAssociations operation to see the association you created. The following is a sample response of what it might look like.

   aws eks list-pod-identity-associations --cluster-name my-eks-cluster
   {
       "associations": [{
           "clusterName": "my-eks-cluster",
           "namespace": "aws-hyperpod",
           "serviceAccount": "hp-training-operator-controller-manager",
           "associationArn": "arn:aws:eks:us-east-2:123456789012:podidentityassociation/my-hyperpod-cluster/a-1a2b3c4d5e6f7g8h9",
           "associationId": "a-1a2b3c4d5e6f7g8h9"
       }]
   }

5. To install the training operator, use the create-addon operation. The --addon-version parameter is optional. If you don’t provide one, the default is the latest version. To get the possible versions, use the DescribeAddonVersions operation.

   aws eks create-addon \
     --cluster-name my-eks-cluster \
     --addon-name amazon-sagemaker-hyperpod-training-operator \
     --resolve-conflicts OVERWRITE

The training operator comes with a number of options with default values that might fit your use case. We recommend that you try out the training operator with default values before changing them. The table below describes all parameters and examples of when you might want to configure each parameter.

HyperPod elastic agent

The HyperPod elastic agent is an extension of PyTorch’s ElasticAgent. It orchestrates lifecycles of training workers on each container and communicates with the HyperPod training operator. To use the HyperPod training operator, you must first install the HyperPod elastic agent into your training image before you can submit and run jobs using the operator. The following is a docker file that installs elastic agent and uses hyperpodrun to create the job launcher.

RUN pip install hyperpod-elastic-agent

ENTRYPOINT ["entrypoint.sh"]
# entrypoint.sh
...
hyperpodrun --nnodes=node_count --nproc-per-node=proc_count \
            --rdzv-backend hyperpod \ # Optional
            ... # Other torchrun args
            # pre-traing arg_group
            --pre-train-script pre.sh --pre-train-args "pre_1 pre_2 pre_3" \
            # post-train arg_group
            --post-train-script post.sh --post-train-args "post_1 post_2 post_3" \
            training.py --script-args

You can now submit jobs with kubectl.

HyperPod elastic agent arguments

The HyperPod elastic agent supports all of the original arguments and adds some additional arguments. The following is all of the arguments available in the HyperPod elastic agent. For more information about PyTorch's Elastic Agent, see their official documentation.

Task governance (optional)

The training operator is integrated with HyperPod task governance, a robust management system designed to streamline resource allocation and ensure efficient utilization of compute resources across teams and projects for your Amazon EKS clusters. To set up HyperPod task governance, see Setup for SageMaker HyperPod task governance.

When submitting a job, make sure you include your queue name and priority class labels of hyperpod-ns-team-name-localqueue and priority-class-name-priority. For example, if you're using Kueue, your labels become the following:

The following is an example of what your configuration file might look like:

apiVersion: sagemaker.amazonaws.com/v1
kind: HyperPodPytorchJob
metadata:
  name: hp-task-governance-sample
  namespace: hyperpod-ns-team-name
  labels:
    kueue.x-k8s.io/queue-name: hyperpod-ns-team-name-localqueue
    kueue.x-k8s.io/priority-class: priority-class-priority
spec:
  nprocPerNode: "1"
  runPolicy:
    cleanPodPolicy: "None"
  replicaSpecs: 
    - name: pods
      replicas: 4
      spares: 2
      template:
        spec:
          containers:
            - name: ptjob
              image: XXXX
              imagePullPolicy: Always
              ports:
                - containerPort: 8080
              resources:
                requests:
                  cpu: "2"

Then use the following kubectl command to apply the YAML file.

kubectl apply -f task-governance-job.yaml

Kueue (optional)

While you can run jobs directly, your organization can also integrate the training operator with Kueue to allocate resources and schedule jobs. Follow the steps below to install Kueue into your HyperPod cluster.