部署自定义模型进行按需型推理

您可以从自定义模型页面中部署自定义模型，如下所示。您也可以从按需自定义模型页面中部署具有相同字段的模型。要找到此页面，请在导航窗格的推理下选择按需自定义模型。

要使用部署自定义模型以进行按需推理 AWS Command Line Interface，请使用带有自定义模型的 Amazon 资源名称 (ARN) 的create-custom-model-deployment命令。此命令使用 CreateCustomModelDeployment API 操作。响应包含部署的 ARN。如果部署处于活动状态，您在发出推理请求时需将此 ARN 用作 modelId。有关使用部署进行推理的信息，请参阅使用部署进行按需型推理。

aws bedrock create-custom-model-deployment \
--model-deployment-name "Unique name" \
--model-arn "Custom Model ARN" \
--description "Deployment description" \
--tags '[
    {
        "key": "Environment",
        "value": "Production"
    },
    {
        "key": "Team",
        "value": "ML-Engineering"
    },
    {
        "key": "Project",
        "value": "CustomerSupport"
    }
]' \
--client-request-token "unique-deployment-token" \
--region region

要部署自定义模型以进行按需推理，请使用带有自定义模型的 Amazon 资源名称 (ARN) 的 CreateCustomModelDeploymentAPI 操作。响应包含部署的 ARN。如果部署处于活动状态，您在发出推理请求时需将此 ARN 用作 modelId。有关使用部署进行推理的信息，请参阅使用部署进行按需型推理。

以下代码演示了如何使用 SDK for Python (Boto3) 部署自定义模型。

def create_custom_model_deployment(bedrock_client):
    """Create a custom model deployment
    Args:
        bedrock_client: A boto3 Amazon Bedrock client for making API calls

    Returns:
        str: The ARN of the new custom model deployment

    Raises:
        Exception: If there is an error creating the deployment
    """

    try:
        response = bedrock_client.create_custom_model_deployment(
            modelDeploymentName="Unique deployment name",
            modelArn="Custom Model ARN",
            description="Deployment description",
            tags=[
                {'key': 'Environment', 'value': 'Production'},
                {'key': 'Team', 'value': 'ML-Engineering'},
                {'key': 'Project', 'value': 'CustomerSupport'}
            ],
            clientRequestToken=f"deployment-{uuid.uuid4()}"
        )

        deployment_arn = response['customModelDeploymentArn']
        print(f"Deployment created: {deployment_arn}")
        return deployment_arn

    except Exception as e:
        print(f"Error creating deployment: {str(e)}")
        raise

要使用删除自定义模型部署 AWS Command Line Interface，请使用带有部署标识符的delete-custom-model-deployment命令。此命令使用 DeleteCustomModelDeployment API 操作。

aws bedrock delete-custom-model-deployment \
--custom-model-deployment-identifier "deployment-arn-or-name" \
--region region

要以编程方式删除自定义模型部署，请使用带有部署的 Amazon 资源名称 (ARN) 或名称的 DeleteCustomModelDeploymentAPI 操作。以下代码演示了如何使用 SDK for Python (Boto3) 删除自定义模型部署。

def delete_custom_model_deployment(bedrock_client):
    """Delete a custom model deployment

    Args:
        bedrock_client: A boto3 Amazon Bedrock client for making API calls

    Returns:
        dict: The response from the delete operation

    Raises:
        Exception: If there is an error deleting the deployment
    """

    try:
        response = bedrock_client.delete_custom_model_deployment(
            customModelDeploymentIdentifier="Deployment identifier"
        )

        print("Deleting deployment...")
        return response

    except Exception as e:
        print(f"Error deleting deployment: {str(e)}")
        raise