本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

設定以角色為基礎的存取控制

叢集管理員使用者還需要為資料科學家使用者設定 Kubernetes 角色型存取控制 (RBAC)，才能使用在透過 Amazon 協調的SageMaker HyperPod CLI HyperPod 叢集上執行工作負載。EKS

選項 1：RBAC使用頭盔圖進行設置

SageMaker HyperPod 服務團隊提供了一個 Helm 子圖表來RBAC設置。如需進一步了解，請參閱 使用 Helm 在 Amazon EKS 叢集上安裝套件。

選項 2：RBAC手動設定

ClusterRoleBinding以最低權限創ClusterRole建並創建Role並RoleBinding具有突變權限。

ClusterRoleBinding為資料科學家IAM角色建立 ClusterRole (F)

建立叢集層級配置檔案cluster_level_config.yaml，如下所示。

kind: ClusterRole
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: hyperpod-scientist-user-cluster-role
rules:
- apiGroups: [""]
  resources: ["pods"]
  verbs: ["list"]
- apiGroups: [""]
  resources: ["nodes"]
  verbs: ["list"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: hyperpod-scientist-user-cluster-role-binding
subjects:
- kind: Group
  name: hyperpod-scientist-user-cluster-level
  apiGroup: rbac.authorization.k8s.io
roleRef:
  kind: ClusterRole
  name: hyperpod-scientist-user-cluster-role # this must match the name of the Role or ClusterRole you wish to bind to
  apiGroup: rbac.authorization.k8s.io

將配置套用至EKS叢集。

kubectl apply -f cluster_level_config.yaml

RoleBinding 在命名空間中建立角色 (A)

這是執行訓練工作的命名空間訓練操作員，而彈性預設會監視。Job 自動恢復只能在kubeflow名稱空間或前aws-hyperpod置詞的命名空間中支援。

建立角色組態檔案namespace_level_role.yaml，如下所示。此範例會在kubeflow命名空間中建立角色

kind: Role
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  namespace: kubeflow
  name: hyperpod-scientist-user-namespace-level-role
###
#  1) add/list/describe/delete pods
#  2) get/list/watch/create/patch/update/delete/describe kubeflow pytroch job
#  3) get pod log
###
rules:
- apiGroups: [""]
  resources: ["pods"]
  verbs: ["create", "get"]
- apiGroups: [""]
  resources: ["nodes"]
  verbs: ["get", "list"]
- apiGroups: [""]
  resources: ["pods/log"]
  verbs: ["get", "list"]
- apiGroups: [""]
  resources: ["pods/exec"]
  verbs: ["get", "create"]
- apiGroups: ["kubeflow.org"]
  resources: ["pytorchjobs", "pytorchjobs/status"]
  verbs: ["get", "list", "create", "delete", "update", "describe"]
- apiGroups: [""]
  resources: ["configmaps"]
  verbs: ["create", "update", "get", "list", "delete"]
- apiGroups: [""]
  resources: ["secrets"]
  verbs: ["create", "get", "list", "delete"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  namespace: kubeflow
  name: hyperpod-scientist-user-namespace-level-role-binding
subjects:
- kind: Group
  name: hyperpod-scientist-user-namespace-level
  apiGroup: rbac.authorization.k8s.io
roleRef:
  kind: Role
  name: hyperpod-scientist-user-namespace-level-role # this must match the name of the Role or ClusterRole you wish to bind to
  apiGroup: rbac.authorization.k8s.io

將配置套用至EKS叢集。

kubectl apply -f namespace_level_role.yaml

為 Kubernetes 群組建立存取項目

RBAC使用上述兩個選項之一進行設定後，請使用下列範例命令取代必要資訊。

aws eks create-access-entry \
    --cluster-name <eks-cluster-name> \
    --principal-arn arn:aws:iam::<AWS_ACCOUNT_ID_SCIENTIST_USER>:role/ScientistUserRole \
    --kubernetes-groups '["hyperpod-scientist-user-namespace-level","hyperpod-scientist-user-cluster-level"]'

對於principal-arn參數，您需要使用IAM科學家的用戶.