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。

# 使用 Boto3 部署編譯的模型
<a name="neo-deployment-hosting-services-boto3"></a>

如果模型是使用 或 Amazon SageMaker AI 主控台編譯的 適用於 Python (Boto3) 的 AWS SDK AWS CLI，您必須滿足[先決條件](https://docs.aws.amazon.com//sagemaker/latest/dg/neo-deployment-hosting-services-prerequisites)區段。請遵循以下步驟，使用[適用於 Python 的 Amazon Web Services SDK (Boto3)](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) 建立和部署 SageMaker Neo 編譯的模型。

**Topics**
+ [部署模型](#neo-deployment-hosting-services-boto3-steps)

## 部署模型
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在您符合[先決條件](https://docs.aws.amazon.com//sagemaker/latest/dg/neo-deployment-hosting-services-prerequisites)之後，請使用 `create_model`、`create_enpoint_config` 和 `create_endpoint` API。

下列範例示範如何使用這些 API 部署使用 Neo 編譯的模型：

```
import boto3
client = boto3.client('sagemaker')

# create sagemaker model
create_model_api_response = client.create_model(
                                    ModelName='my-sagemaker-model',
                                    PrimaryContainer={
                                        'Image': <insert the ECR Image URI>,
                                        'ModelDataUrl': 's3://path/to/model/artifact/model.tar.gz',
                                        'Environment': {}
                                    },
                                    ExecutionRoleArn='ARN for AmazonSageMaker-ExecutionRole'
                            )

print ("create_model API response", create_model_api_response)

# create sagemaker endpoint config
create_endpoint_config_api_response = client.create_endpoint_config(
                                            EndpointConfigName='sagemaker-neomxnet-endpoint-configuration',
                                            ProductionVariants=[
                                                {
                                                    'VariantName': <provide your variant name>,
                                                    'ModelName': 'my-sagemaker-model',
                                                    'InitialInstanceCount': 1,
                                                    'InstanceType': <provide your instance type here>
                                                },
                                            ]
                                       )

print ("create_endpoint_config API response", create_endpoint_config_api_response)

# create sagemaker endpoint
create_endpoint_api_response = client.create_endpoint(
                                    EndpointName='provide your endpoint name',
                                    EndpointConfigName=<insert your endpoint config name>,
                                )

print ("create_endpoint API response", create_endpoint_api_response)
```

**注意**  
`AmazonSageMakerFullAccess` 和 `AmazonS3ReadOnlyAccess` 政策必須連接到 `AmazonSageMaker-ExecutionRole` IAM 角色。

如需 `create_model`、`create_endpoint_config` 和 `create_endpoint` API 的完整語法，請分別參閱 [https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_model](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_model)、[https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_endpoint_config](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_endpoint_config) 以及 [https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_endpoint](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#SageMaker.Client.create_endpoint)。

如果您未使用 SageMaker AI 訓練模型，請指定下列環境變數：

------
#### [ MXNet and PyTorch ]

```
"Environment": {
    "SAGEMAKER_PROGRAM": "inference.py",
    "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code",
    "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
    "SAGEMAKER_REGION": "insert your region",
    "MMS_DEFAULT_RESPONSE_TIMEOUT": "500"
}
```

------
#### [ TensorFlow ]

```
"Environment": {
    "SAGEMAKER_PROGRAM": "inference.py",
    "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code",
    "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
    "SAGEMAKER_REGION": "insert your region"
}
```

------

 如果您使用 SageMaker AI 訓練模型，請將環境變數 `SAGEMAKER_SUBMIT_DIRECTORY` 指定為包含訓練指令碼的完整 Amazon S3 儲存貯體 URI。