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# 创建自定义模型 (AWS SDKs)
<a name="create-custom-model-sdks"></a>

要使用存储在 Amazon S3 中的 SageMaker 经过人工智能训练的 Amazon Nova 模型创建自定义模型，请使用 [CreateCustomModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateCustomModel.html)API 操作。您可以使用以下代码，通过适用于 Python 的 SDK（Boto3）创建自定义模型。这段代码创建自定义模型，然后检查其状态，直到模型处于 `ACTIVE` 状态并可供使用。

要使用代码，请更新以下参数。这段示例代码还包括可选参数，例如 `clientRequestToken` 用于幂等性，`modelTags` 用于资源标记。
+ **modelName** – 为模型指定唯一名称。
+ **s3uri** — 指定存储模型工件的亚马逊托管的 Amazon S3 存储桶的路径。 SageMaker 当你运行第一个 AI 训练作业时， SageMaker AI 会创建这个存储桶。
+ **roleArn** – 指定 IAM 服务角色的 Amazon 资源名称（ARN），Amazon Bedrock 将代入该角色来代表您执行任务。有关创建此角色的更多信息，请参阅 [为导入预训练模型创建服务角色](model-import-iam-role.md)。
+ **modelKmsKeyArn**（可选）— 在 Amazon Bedrock 中指定用于加密模型的 AWS KMS 密钥。如果您不提供 AWS KMS 密钥，Amazon Bedrock 会使用 AWS托管密 AWS KMS 钥对模型进行加密。有关加密的信息，请参阅[导入的自定义模型的加密](encryption-import-model.md)。

创建自定义模型后，模型将显示在[ListCustomModels](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListCustomModels.html)响应中，并显示`customizationType`为`imported`。要跟踪新模型的状态，您可以使用 [GetCustomModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetCustomModel.html)API 操作。

```
import boto3
import uuid
from botocore.exceptions import ClientError
import time

def create_custom_model(bedrock_client):
    """
    Creates a custom model in Amazon Bedrock from a SageMaker AI-trained Amazon Nova model stored in Amazon S3.
    Args:
        bedrock_client: The Amazon Bedrock client instance
    Returns:
        dict: Response from the CreateCustomModel API call
    """
    try:
        # Create a unique client request token for idempotency
        client_request_token = str(uuid.uuid4())

        # Define the model source configuration
        model_source_config = {
            's3DataSource': {
                's3Uri': '{{s3://amzn-s3-demo-bucket/folder/}}',
            }
        }

        # Create the custom model
        response = bedrock_client.create_custom_model(
            # Required parameters
            modelName='{{modelName}}',
            roleArn='{{serviceRoleArn}}',
            modelSourceConfig=model_source_config,

            # Optional parameters
            clientRequestToken=client_request_token,
            modelKmsKeyArn='{{keyArn}}',
            modelTags=[
                {
                    'key': 'Environment',
                    'value': 'Production'
                },
                {
                    'key': 'Project',
                    'value': 'AIInference'
                }
            ]
        )

        print(f"Custom model creation initiated. Model ARN: {response['modelArn']}")

        return response

    except ClientError as e:
        print(f"Error creating custom model: {e}")
        raise

def list_custom_models(bedrock_client):
    """
    Lists all custom models in Amazon Bedrock.

    Args:
        bedrock_client: An Amazon Bedrock client.

    Returns:
        dict: Response from the ListCustomModels API call

    """

    try:
        response = bedrock_client.list_custom_models()
        print(f"Total number of custom models: {len(response['modelSummaries'])}")

        for model in response['modelSummaries']:
            print("ARN: " + model['modelArn'])
            print("Name: " + model['modelName'])
            print("Status: " + model['modelStatus'])
            print("Customization type: " + model['customizationType'])
            print("------------------------------------------------------")

        return response

    except ClientError as e:
        print(f"Error listing custom models: {e}")
        raise

def check_model_status(bedrock_client, model_arn):
    """
    Checks the status of a custom model creation.

    Args:
        model_arn (str): The ARN of the custom model
        bedrock_client: An Amazon Bedrock client.

    Returns:
        dict: Response from the GetCustomModel API call

    """

    try:
        max_time = time.time() + 60 * 60  # 1 hour

        while time.time() < max_time:
            response = bedrock_client.get_custom_model(modelIdentifier=model_arn)
            status = response.get('modelStatus')
            print(f"Job status: {status}")
            if status == 'Failed':
                print(f"Failure reason: {response.get('failureMessage')}")
                break
            if status == 'Active':
                print("Model is ready for use.")
                break
            time.sleep(60)

    except ClientError as e:
        print(f"Error checking model status: {e}")
        raise


def main():
    bedrock_client = boto3.client(service_name='bedrock', region_name='{{REGION}}')
    
    # Create the custom model
    model_arn = create_custom_model(bedrock_client)["modelArn"]

    # Check the status of the model
    if model_arn:
        check_model_status(bedrock_client, model_arn)

    # View all custom models
    list_custom_models(bedrock_client)


if __name__ == "__main__":
    main()
```