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# 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
<a name="sagemaker-hyperpod-eks-cluster-observability-model"></a>

SageMaker HyperPod 使用 Amazon EKS 编排的集群可以与 Amazon Studi [MLflow o 上的应用程序](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow.html)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 AWS CLI 设置 MLflow 服务器**

集群管理员必须创建 MLflow 跟踪服务器。

1. 按照[使用 CL SageMaker I 创建 MLflow 跟踪服务器中的说明创建 A AWS I 跟踪服务器](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server-cli.html#mlflow-create-tracking-server-cli-infra-setup)。

1. 确保[https://docs.aws.amazon.com/eks/latest/APIReference/API_auth_AssumeRoleForPodIdentity.html](https://docs.aws.amazon.com/eks/latest/APIReference/API_auth_AssumeRoleForPodIdentity.html)权限存在于的 IAM 执行角色中 SageMaker HyperPod。

1. 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
   ```

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"
   ```

1. 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 它们是根据设置[ MLflow 基础架构](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server-cli.html#mlflow-create-tracking-server-cli-infra-setup)的说明创建 MLflow 服务器期间在 MLflow 服务器上设置的 S3 存储桶和 S3 存储桶。

1. 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
   ```

1. 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
   ```

1. 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 

1. 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()
   ```

1. 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》](https://docs.aws.amazon.com/AmazonECR/latest/userguide/docker-push-ecr-image.html)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](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-track-experiments.html#mlflow-track-experiments-install-plugin)。

1. 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
   ```

1. 使用 YAML 文件启动作业，如下所示。

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

1. 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
   ```