对由 Amazon EKS 编排的 SageMaker HyperPod 集群上训练作业的可观察性进行建模

SageMaker HyperPod 使用 Amazon EKS 编排的集群可以与 Amazon Studi MLflow o 上的应用程序集成。 SageMaker 集群管理员设置 MLflow 服务器并将其与 SageMaker HyperPod 集群连接。数据科学家可以深入了解模型

使用 Amazon CLI 设置 MLflow 服务器

MLflow 跟踪服务器应由集群管理员创建。

按照使用 CL SageMaker I 创建 MLflow 跟踪服务器中的说明创建 A Amazon I 跟踪服务器。
确保eks-auth:AssumeRoleForPodIdentity权限存在于的 IAM 执行角色中 SageMaker HyperPod。

如果 EKS 集群上尚未安装 eks-pod-identity-agent 插件，请在 EKS 集群上安装此插件。


aws eks create-addon \
    --cluster-name <eks_cluster_name> \
    --addon-name eks-pod-identity-agent \
    --addon-version vx.y.z-eksbuild.1

为 Pod 调用的新角色创建一个trust-relationship.json文件 MLflow APIs。


cat >trust-relationship.json <<EOF
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "AllowEksAuthToAssumeRoleForPodIdentity",
            "Effect": "Allow",
            "Principal": {
                "Service": "pods.eks.amazonaws.com"

            },
            "Action": [
                "sts:AssumeRole",
                "sts:TagSession"
            ]
        }
    ]
}
EOF

运行以下代码创建角色并附加信任关系。


aws iam create-role --role-name hyperpod-mlflow-role \
    --assume-role-policy-document file://trust-relationship.json \
    --description "allow pods to emit mlflow metrics and put data in s3"

创建以下策略，授予 Pod 调用所有 sagemaker-mlflow 操作和将模型构件放入 S3 的权限。跟踪服务器中已存在 S3 权限，但是如果模型工件太大，则会直接从 MLflow 代码调用 s3 来上传工件。


cat >hyperpod-mlflow-policy.json <<EOF
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "sagemaker-mlflow:AccessUI",
                "sagemaker-mlflow:CreateExperiment",
                "sagemaker-mlflow:SearchExperiments",
                "sagemaker-mlflow:GetExperiment",
                "sagemaker-mlflow:GetExperimentByName",
                "sagemaker-mlflow:DeleteExperiment",
                "sagemaker-mlflow:RestoreExperiment",
                "sagemaker-mlflow:UpdateExperiment",
                "sagemaker-mlflow:CreateRun",
                "sagemaker-mlflow:DeleteRun",
                "sagemaker-mlflow:RestoreRun",
                "sagemaker-mlflow:GetRun",
                "sagemaker-mlflow:LogMetric",
                "sagemaker-mlflow:LogBatch",
                "sagemaker-mlflow:LogModel",
                "sagemaker-mlflow:LogInputs",
                "sagemaker-mlflow:SetExperimentTag",
                "sagemaker-mlflow:SetTag",
                "sagemaker-mlflow:DeleteTag",
                "sagemaker-mlflow:LogParam",
                "sagemaker-mlflow:GetMetricHistory",
                "sagemaker-mlflow:SearchRuns",
                "sagemaker-mlflow:ListArtifacts",
                "sagemaker-mlflow:UpdateRun",
                "sagemaker-mlflow:CreateRegisteredModel",
                "sagemaker-mlflow:GetRegisteredModel",
                "sagemaker-mlflow:RenameRegisteredModel",
                "sagemaker-mlflow:UpdateRegisteredModel",
                "sagemaker-mlflow:DeleteRegisteredModel",
                "sagemaker-mlflow:GetLatestModelVersions",
                "sagemaker-mlflow:CreateModelVersion",
                "sagemaker-mlflow:GetModelVersion",
                "sagemaker-mlflow:UpdateModelVersion",
                "sagemaker-mlflow:DeleteModelVersion",
                "sagemaker-mlflow:SearchModelVersions",
                "sagemaker-mlflow:GetDownloadURIForModelVersionArtifacts",
                "sagemaker-mlflow:TransitionModelVersionStage",
                "sagemaker-mlflow:SearchRegisteredModels",
                "sagemaker-mlflow:SetRegisteredModelTag",
                "sagemaker-mlflow:DeleteRegisteredModelTag",
                "sagemaker-mlflow:DeleteModelVersionTag",
                "sagemaker-mlflow:DeleteRegisteredModelAlias",
                "sagemaker-mlflow:SetRegisteredModelAlias",
                "sagemaker-mlflow:GetModelVersionByAlias"
            ],
            "Resource": "arn:aws:sagemaker:us-west-2:111122223333:mlflow-tracking-server/<ml tracking server name>"
        },
        {
            "Effect": "Allow",
            "Action": [
                "s3:PutObject"
            ],
            "Resource": "arn:aws:s3:::<mlflow-s3-bucket_name>"
        }
    ]
}
EOF

注意

ARNs 应该是 MLflow 服务器中的存储桶和 S3 存储桶，在您创建 MLflow 服务器期间按照设置 MLflow 基础架构的说明在服务器上设置的。

使用上一步中保存的策略文档，将 mlflow-metrics-emit-policy 策略附加到 hyperpod-mlflow-role。


aws iam put-role-policy \
  --role-name hyperpod-mlflow-role \
  --policy-name mlflow-metrics-emit-policy \
  --policy-document file://hyperpod-mlflow-policy.json

为 Pod 创建一个 Kubernetes 服务账号来访问服务器。 MLflow


cat >mlflow-service-account.yaml <<EOF
apiVersion: v1
kind: ServiceAccount
metadata:
  name: mlflow-service-account
  namespace: kubeflow
EOF

运行以下命令应用到 EKS 集群。


kubectl apply -f mlflow-service-account.yaml

创建容器组身份关联。


aws eks create-pod-identity-association \
    --cluster-name EKS_CLUSTER_NAME \
    --role-arn arn:aws:iam::111122223333:role/hyperpod-mlflow-role \
    --namespace kubeflow \
    --service-account mlflow-service-account

将训练作业中的指标收集到 MLflow服务器

数据科学家需要设置训练脚本和 docker 镜像，以便向服务器发送指标。 MLflow

在训练脚本的开头添加以下几行。


import mlflow

# Set the Tracking Server URI using the ARN of the Tracking Server you created
mlflow.set_tracking_uri(os.environ['MLFLOW_TRACKING_ARN'])
# Enable autologging in MLflow
mlflow.autolog()

使用训练脚本构建 Docker 映像，并推送到 Amazon ECR。获取 ECR 容器的 ARN。有关构建和推送 Docker 映像的更多信息，请参阅《ECR 用户指南》中的推送 Docker 映像。

提示
确保在 Docker 文件中添加 mlflow 和 sagemaker-mlflow 软件包的安装。要详细了解软件包的安装、要求和软件包的兼容版本，请参阅安装 MLflow 和 SageMaker AI MLflow 插件。

在训练作业 Pod 中添加服务账号使其能够访问 hyperpod-mlflow-role。这允许 Pod 调用 MLflow APIs。运行以下 SageMaker HyperPod CLI 作业提交模板。创建此文件，文件名为 mlflow-test.yaml。


defaults:
 - override hydra/job_logging: stdout

hydra:
 run:
  dir: .
 output_subdir: null

training_cfg:
 entry_script: ./train.py
 script_args: []
 run:
  name: test-job-with-mlflow # Current run name
  nodes: 2 # Number of nodes to use for current training
  # ntasks_per_node: 1 # Number of devices to use per node
cluster:
 cluster_type: k8s # currently k8s only
 instance_type: ml.c5.2xlarge
 cluster_config:
  # name of service account associated with the namespace
  service_account_name: mlflow-service-account
  # persistent volume, usually used to mount FSx
  persistent_volume_claims: null
  namespace: kubeflow
  # required node affinity to select nodes with SageMaker HyperPod
  # labels and passed health check if burn-in enabled
  label_selector:
      required:
          sagemaker.amazonaws.com/node-health-status:
              - Schedulable
      preferred:
          sagemaker.amazonaws.com/deep-health-check-status:
              - Passed
      weights:
          - 100
  pullPolicy: IfNotPresent # policy to pull container, can be Always, IfNotPresent and Never
  restartPolicy: OnFailure # restart policy

base_results_dir: ./result # Location to store the results, checkpoints and logs.
container: 111122223333.dkr.ecr.us-west-2.amazonaws.com/tag # container to use

env_vars:
 NCCL_DEBUG: INFO # Logging level for NCCL. Set to "INFO" for debug information
 MLFLOW_TRACKING_ARN: arn:aws:sagemaker:us-west-2:11112223333:mlflow-tracking-server/tracking-server-name

使用 YAML 文件启动作业，如下所示。


hyperpod start-job --config-file /path/to/mlflow-test.yaml

为 MLflow 跟踪服务器生成预签名 URL。您可以在浏览器上打开链接，开始跟踪您的训练作业。


aws sagemaker create-presigned-mlflow-tracking-server-url \                          
    --tracking-server-name "tracking-server-name" \
    --session-expiration-duration-in-seconds 1800 \
    --expires-in-seconds 300 \
    --region region

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可观察性

集群可观测性

对由 Amazon EKS 编排的 SageMaker HyperPod 集群上训练作业的可观察性进行建模

注意

提示