AWS nova documentation change
Summary
Restructured documentation by removing detailed deployment/usage/deletion procedures and replacing them with links to separate HTML files. The main page now focuses on supported models and basic concepts rather than implementation steps.
Security assessment
The changes are organizational/structural improvements to documentation navigation. No security vulnerabilities, mitigations, or security features were added/removed. The removed content was procedural documentation about model deployment lifecycle management without security-specific context.
Diff
diff --git a/nova/latest/userguide/deploy-custom-model.md b/nova/latest/userguide/deploy-custom-model.md index c05e12f93..2e2c79d82 100644 --- a//nova/latest/userguide/deploy-custom-model.md +++ b//nova/latest/userguide/deploy-custom-model.md @@ -5 +5 @@ -Supported modelsDeploy a custom modelUse a deployment for on-demand inferenceDelete a custom model deployment +Supported models @@ -17 +17 @@ To set up on-demand inference for a custom model, you deploy the model with a cu - * Deploy a custom model + * [Deploy a custom model](./deploying-custom-model.html) @@ -19 +19 @@ To set up on-demand inference for a custom model, you deploy the model with a cu - * Use a deployment for on-demand inference + * [Use a deployment for on-demand inference](./use-custom-model-on-demand.html) @@ -21 +21 @@ To set up on-demand inference for a custom model, you deploy the model with a cu - * Delete a custom model deployment + * [Delete a custom model deployment](./delete-custom-model-deployment.html) @@ -41,186 +40,0 @@ You can set up on-demand inference for the following models: -## Deploy a custom model - -You can deploy a custom model with the Amazon Bedrock console, AWS Command Line Interface, or AWS SDKs. For information about using the deployment for inference, see [Use a deployment for on-demand inference](https://docs.aws.amazon.com/bedrock/latest/userguide/use-custom-model-on-demand.html). - -Console - - -You deploy a custom model from the **Custom models** page as follows. You can also deploy a model from the **Custom model on-demand** page with the same fields. To find this page, in **Inference and Assessment** in the navigation pane, choose **Custom model on-demand**. - -###### To deploy a custom model - - 1. Sign in to the AWS Management Console using an [IAM role with Amazon Bedrock permissions](./getting-started.html), and open the Amazon Bedrock console at [https://console.aws.amazon.com/nova/](https://console.aws.amazon.com/nova/). - - 2. From the left navigation pane, choose **Custom models** under **Foundation models**. - - 3. In the **Models** tab, choose the radio button for the model you want to deploy. - - 4. Choose **Set up inference** and choose **Deploy for on-demand**. - - 5. In **Deployment details** , provide the following information: - - * **Deployment Name** (required) – Enter a unique name for your deployment. - - * **Description** (optional) – Enter a description for your deployment. - - * **Tags** (optional) – Add tags for cost allocation and resource management. - - 6. Choose **Create**. When the status shows `Completed`, your custom model is ready for on-demand inference. For more information about using the custom model, see [Use a deployment for on-demand inference](https://docs.aws.amazon.com/bedrock/latest/userguide/use-custom-model-on-demand.html). - - - - -AWS Command Line Interface - - -To deploy a custom model for on-demand inference using the AWS Command Line Interface, use the `create-custom-model-deployment` command with your custom model's Amazon Resource Name (ARN). This command uses the [CreateCustomModelDeployment](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateCustomModelDeployment.html) API operation. It returns the deployment's ARN that you can use as the `modelId` when making inference requests. For information about using the deployment for inference, see [Use a deployment for on-demand inference](https://docs.aws.amazon.com/bedrock/latest/userguide/use-custom-model-on-demand.html). - - - 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 - -AWS SDKs - - -To deploy a custom model for on-demand inference, use the [CreateCustomModelDeployment](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateCustomModelDeployment.html) API operation with your custom model's Amazon Resource Name (ARN). The response returns the deployment's ARN that you can use as the `modelId` when making inference requests. For information about using the deployment for inference, see [Use a deployment for on-demand inference](https://docs.aws.amazon.com/bedrock/latest/userguide/use-custom-model-on-demand.html). - -The following code shows how to use the SDK for Python (Boto3) to deploy a custom model. - - - def create_custom_model_deployment(bedrock_client): - """Create a custom model deployment - Args: - bedrock_client: A boto3 Bedrock client for making API calls - - Returns: - str: The ARN of the created 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 - -## Use a deployment for on-demand inference - -After you deploy your custom model for on-demand inference, you can use it to generate responses by making inference requests. For `InvokeModel` or `Converse` operations, you use the deployment Amazon Resource Name (ARN) as the `modelId`. - -For information about making inference requests, see the following topics: - - * [Submit prompts and generate responses with model inference](https://docs.aws.amazon.com/bedrock/latest/userguide/inference.html) - - * [Prerequisites for running model inference](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-prereq.html) - - * [Submit prompts and generate responses using the API](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-api.html) - - - - -## Delete a custom model deployment - -After you are finished using your model for on-demand inference, you can delete the deployment. After you delete the deployment, you can't use it for on-demand inference but it doesn't delete the underlying custom model. - -You can delete a custom model deployment with the Amazon Bedrock console, AWS Command Line Interface, or AWS SDKs. - -###### Important - -Deleting a custom model deployment is irreversible. Make sure you no longer need the deployment before proceeding with the deletion. If you need to use the custom model for on-demand inference again, you must create a new deployment. - -Console - - -###### To delete a custom model deployment - - 1. In the navigation pane, under **Inference and Assessment** , choose **Custom model on-demand**. - - 2. Choose the custom model deployment you want to delete. - - 3. Choose **Delete**. - - 4. In the confirmation dialog, enter the deployment name to confirm the deletion. - - 5. Choose **Delete** to confirm. - - - - -The deployment status will change to `Deleting` while the deletion is in progress. Once completed, the deployment will be removed from the list. - -AWS Command Line Interface - - -To delete a custom model deployment using the AWS Command Line Interface, use the `delete-custom-model-deployment` command with your deployment identifier. - - - aws bedrock delete-custom-model-deployment \ - --custom-model-deployment-identifier "deployment-arn-or-name" \ - --region region - -AWS SDKs - - -To delete a custom model deployment programmatically, use the [DeleteCustomModelDeployment](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_DeleteCustomModelDeployment.html) API operation with the deployment's Amazon Resource Name (ARN) or name. The following code shows how to use the SDK for Python (Boto3) to delete a custom model deployment. - - - def delete_custom_model_deployment(bedrock_client): - """Delete a custom model deployment - - Args: - bedrock_client: A boto3 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(f"Deployment deletion initiated") - return response - - except Exception as e: