部署自訂模型以進行隨需推論

您可以如下所示從自訂模型頁面部署自訂模型。您也可以從具有相同欄位的隨需自訂模型頁面部署模型。若要尋找此頁面，請在導覽窗格的推論下，選擇隨需自訂模型。

若要使用 部署用於隨需推論的自訂模型 AWS Command Line Interface，請使用 create-custom-model-deployment命令搭配自訂模型的 Amazon Resource Name (ARN)。此命令使用 CreateCustomModelDeployment API 操作。此回應包含部署的 ARN。部署處於作用中狀態時，您可以在提出推論請求時使用此 ARN 作為 modelId。如需使用部署進行推論的相關資訊，請參閱使用部署進行隨需推論。

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

若要部署用於隨需推論的自訂模型，請使用 CreateCustomModelDeployment API 操作搭配自訂模型的 Amazon Resource Name (ARN)。此回應包含部署的 ARN。部署處於作用中狀態時，您可以在提出推論請求時使用此 ARN 作為 modelId。如需使用部署進行推論的相關資訊，請參閱使用部署進行隨需推論。

下列程式碼說明如何使用適用於 Python (Boto3) 的 SDK 部署自訂模型。

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

若要使用 刪除自訂模型部署 AWS Command Line Interface，請使用 delete-custom-model-deployment命令搭配您的部署識別符。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

若要以程式設計方式刪除自訂模型部署，請使用 DeleteCustomModelDeployment API 操作搭配部署的 Amazon Resource Name (ARN) 或名稱。下列程式碼說明如何使用適用於 Python (Boto3) 的 SDK 來刪除自訂模型部署。

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