AWS AI ML

Deploy Nova LoRA to Bedrock (PySDK BedrockModelBuilder)

Scenario

• Model Type: Nova

• Fine-tuning Method: LoRA

• Deployment Target: Bedrock Custom Model

• Approach: SageMaker PySdk BedrockModelBuilder

Overview

Uses the SageMaker PySdk BedrockModelBuilder to deploy a Nova fine-tuned LoRA model to Bedrock as a Custom Model. The builder auto-detects Nova models and calls CreateCustomModel.

Required inputs (collected in the steps below):

• Training job name

• Custom model name

• IAM role ARN

• AWS region

Prerequisites

Requires SageMaker Python SDK >= 3.7.0 with BedrockModelBuilder Nova support (installed by Cell 1).

Workflow

Important Instructions

• Make sure to use dedicated tools instead of bash commands whenever possible

Step 1: Gather Training Job Name

The training job name was identified in Step 1 of the main workflow. Confirm you have it.

Step 2: Gather Custom Model Name

For this step, you need: a name for the deployed custom model.

Suggest a name based on the training job or use case, e.g., nova-micro-bedrock-<timestamp>. Ask the user to confirm or provide their own.

⏸ Wait for user before moving on.

Step 3: Verify IAM Role

Use the IAM role from the training job (extracted in Step 1 of the main workflow via describe-training-job). We'll assume this role has the necessary permissions for Bedrock deployment.

Step 4: Confirm Region

The region was identified in Step 1 of the main workflow. Nova → Bedrock deployment is supported in us-east-1 (this may be outdated — region availability can change and there is no API to query it). If the training job is in a different region, warn the user that Bedrock deployment for this model is likely limited to us-east-1; if an import is attempted elsewhere and fails (note: Nova → Bedrock uses Custom Model Import under the hood, so an unsupported region typically returns an access/authorization-style error rather than a clear region error), confirm it is not supported there.

Step 5: Confirm Configuration

"Here's the deployment setup:

• Deployment target: Bedrock (Custom Model)

• Training Job: [job-name]

• Custom Model Name: [name]

• IAM Role: [arn]

• Region: us-east-1

Does this look right?"

⏸ Wait for user approval.

Step 6: Generate Code

Read ../references/code_output_guide.md for output format rules.

If a project directory already exists (from earlier in the workflow), use it. Otherwise, load the directory-management reference to set one up.

⏸ Wait for user.

Code Structure

Markdown Header

{
  "cell_type": "markdown",
  "metadata": {},
  "source": [
    "# Deploy Nova to Bedrock"
  ]
}

Cells

Each cell's content comes from ../code_templates/deploy-nova-bedrock.py, split on the # Cell N: comments. Each marker starts a new notebook cell — everything between one marker and the next becomes that cell's content.

• Cell 1: Setup (pip install)

• Cell 2: Configuration (env vars, imports, placeholders)

• Cell 3: Build and Deploy to Bedrock (blocks until deployment is Active)

• Cell 4: Test Inference

• Cell 5: Save Manifest

Placeholders

Cell 2:

• [REGION] → AWS region (us-east-1)

• [TRAINING_JOB_NAME] → SageMaker training job name

• [ROLE_ARN] → IAM role ARN

• [CUSTOM_MODEL_NAME] → Name for the custom model

All other cells have no placeholders.

Step 7: Provide Run Instructions

To run:
1. Cell 1 — install SDK packages
2. Cell 2 — set configuration values
3. Cell 3 — creates custom model via BedrockModelBuilder and deploys (blocks until Active)
4. Cell 4 — test inference with a sample prompt via Converse API
5. Cell 5 — saves the deployment manifest to `manifests/deploy-<name>.json`

Common Issues

• "ServiceQuotaExceededException: The number of custom models in Creating status has reached the quota limit": Too many concurrent model creations. Wait for in-progress models to finish, or delete old custom models.

• "No module named 'sagemaker.serve.bedrock_model_builder'": Re-run Cell 1 to install the required packages, then restart the kernel.

• "Access denied to S3": Add S3 read permissions to the IAM role for the model artifacts bucket.

• "Provided IAM role could not be assumed": Ensure role has trust policy for bedrock.amazonaws.com.

• Deployment status "Failed": Check CloudTrail for the CreateCustomModel event to see the failure reason.