使用陰影變體測試模型

您可以使用 SageMaker AI Model Shadow Deployments 來建立長時間執行的陰影變體，在將模型服務堆疊的任何新候選元件提升至生產環境之前，驗證其內容。下圖顯示更詳細的陰影收體運作情形。

部署陰影變體

下列程式碼範例示範如何以程式設計方式部署陰影變體。請將範例中的使用者預留位置文字取代為您自己的資訊。

1. 建立兩個 SageMaker AI 模型：一個用於生產變體，另一個用於陰影變體。

   import boto3
   from sagemaker import get_execution_role, Session
                   
   aws_region = "aws-region"
   
   boto_session = boto3.Session(region_name=aws_region)
   sagemaker_client = boto_session.client("sagemaker")
   
   role = get_execution_role()
   
   bucket = Session(boto_session).default_bucket()
   
   model_name1 = "name-of-your-first-model"
   model_name2 = "name-of-your-second-model"
   
   sagemaker_client.create_model(
       ModelName = model_name1,
       ExecutionRoleArn = role,
       Containers=[
           {
               "Image": "ecr-image-uri-for-first-model",
               "ModelDataUrl": "s3-location-of-trained-first-model" 
           }
       ]
   )
   
   sagemaker_client.create_model(
       ModelName = model_name2,
       ExecutionRoleArn = role,
       Containers=[
           {
               "Image": "ecr-image-uri-for-second-model",
               "ModelDataUrl": "s3-location-of-trained-second-model" 
           }
       ]
   )
                   

2. 建立端點組態。在組態中指定生產和陰影變體。

   endpoint_config_name = name-of-your-endpoint-config
   
   create_endpoint_config_response = sagemaker_client.create_endpoint_config(
       EndpointConfigName=endpoint_config_name,
       ProductionVariants=[
           {
               "VariantName": name-of-your-production-variant,
               "ModelName": model_name1,
               "InstanceType": "ml.m5.xlarge",
               "InitialInstanceCount": 1,
               "InitialVariantWeight": 1,
           }
       ],
       ShadowProductionVariants=[
           {
               "VariantName": name-of-your-shadow-variant,
               "ModelName": model_name2,
               "InstanceType": "ml.m5.xlarge",
               "InitialInstanceCount": 1,
               "InitialVariantWeight": 1,
           }
      ]
   )
                   

3. 建立端點。

   create_endpoint_response = sm.create_endpoint(
       EndpointName=name-of-your-endpoint,
       EndpointConfigName=endpoint_config_name,
   )