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。

# 建立一個端點組態


建立模型後，使用 [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html) 建立一個端點組態。Amazon SageMaker AI 託管服務使用此組態部署模型。在組態中，您可以識別一或多個使用 [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModel.html) 建立的模型，部署您希望 Amazon SageMaker AI 佈建的資源。指定 `AsyncInferenceConfig` 物件並為 `OutputConfig` 提供一個輸出 Amazon S3 位置。您可以選用指定 [Amazon SNS](https://docs.aws.amazon.com/sns/latest/dg/welcome.html) 主題，以傳送預測結果通知。如需 Amazon SNS 主題的更多相關資訊，請參閱 [設定 Amazon SNS](https://docs.aws.amazon.com/sns/latest/dg/sns-configuring.html)。

下列範例說明如何使用 適用於 Python (Boto3) 的 AWS SDK建立端點組態：

```
import datetime
from time import gmtime, strftime

# Create an endpoint config name. Here we create one based on the date  
# so it we can search endpoints based on creation time.
endpoint_config_name = f"XGBoostEndpointConfig-{strftime('%Y-%m-%d-%H-%M-%S', gmtime())}"

# The name of the model that you want to host. This is the name that you specified when creating the model.
model_name='<The_name_of_your_model>'

create_endpoint_config_response = sagemaker_client.create_endpoint_config(
    EndpointConfigName=endpoint_config_name, # You will specify this name in a CreateEndpoint request.
    # List of ProductionVariant objects, one for each model that you want to host at this endpoint.
    ProductionVariants=[
        {
            "VariantName": "variant1", # The name of the production variant.
            "ModelName": model_name, 
            "InstanceType": "ml.m5.xlarge", # Specify the compute instance type.
            "InitialInstanceCount": 1 # Number of instances to launch initially.
        }
    ],
    AsyncInferenceConfig={
        "OutputConfig": {
            # Location to upload response outputs when no location is provided in the request.
            "S3OutputPath": f"s3://{s3_bucket}/{bucket_prefix}/output"
            # (Optional) specify Amazon SNS topics
            "NotificationConfig": {
                "SuccessTopic": "arn:aws:sns:aws-region:account-id:topic-name",
                "ErrorTopic": "arn:aws:sns:aws-region:account-id:topic-name",
            }
        },
        "ClientConfig": {
            # (Optional) Specify the max number of inflight invocations per instance
            # If no value is provided, Amazon SageMaker will choose an optimal value for you
            "MaxConcurrentInvocationsPerInstance": 4
        }
    }
)

print(f"Created EndpointConfig: {create_endpoint_config_response['EndpointConfigArn']}")
```

在上述範例中，您可以為 `AsyncInferenceConfig` 欄位的 `OutputConfig` 指定下列金鑰：
+ `S3OutputPath`：請求中未提供位置時，上傳回應輸出的位置。
+ `NotificationConfig`: (選用) 在推論請求成功 (`SuccessTopic`) 或失敗 (`ErrorTopic`) 時，會傳送通知給您的 SNS 主題 。

您還可以為`AsyncInferenceConfig` 欄位中的 `ClientConfig` 指定下列選用引數：
+ `MaxConcurrentInvocationsPerInstance`：(選用) SageMaker AI 用戶端傳送至模型容器的並行請求數目上限。