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# 测试带有阴影变体的模型
<a name="model-shadow-deployment"></a>

 您可以使用 SageMaker AI 模型阴影部署来创建长期运行的阴影变体，以便在将模型服务堆栈的任何新备用组件升级到生产环境之前对其进行验证。下图更详细地说明了阴影变体的工作方式。

![\[阴影变体的详细信息。\]](http://docs.amazonaws.cn/sagemaker/latest/dg/images/juxtaposer/shadow-variant.png)


## 部署阴影变体
<a name="model-shadow-deployment-deploy"></a>

 以下代码示例显示了如何通过编程方式部署阴影变体。请将示例中的*用户占位符文本*替换为您自己的信息。

1.  创建两个 SageMaker AI 模型：一个用于生产变体，另一个用于阴影变体。

   ```
   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" 
           }
       ]
   )
   ```

1.  创建端点配置。在配置中指定您的生产变体和阴影变体。

   ```
   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,
           }
      ]
   )
   ```

1. 创建端点。

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