Package Model - Amazon SageMaker

Package Model

SageMaker Edge Manager packaging jobs take Amazon SageMaker Neo–compiled models and make any changes necessary to deploy the model with the inference engine, Edge Manager agent.


To package a model, you must do the following:

  1. Compile your machine learning model with SageMaker Neo.

    If you have not already done so, compile your model with SageMaker Neo. For more information on how to compile your model, see Compile and Deploy Models with Neo. If you are first-time user of SageMaker Neo, go through Getting Started with Neo Edge Devices.

  2. Get the name of your compilation job.

    Provide the name of the compilation job name you used when you compiled your model with SageMaker Neo. Open the SageMaker console at and choose Compilation jobs to find a list of compilations that have been submitted to your AWS account. The names of submitted compilation jobs are in the Name column.

  3. Get your IAM ARN.

    You need an Amazon Resource Name (ARN) of an IAM role that you can use to download and upload the model and contact SageMaker Neo.

    Use one of the following methods to get your IAM ARN:

    • Programmatically with the SageMaker Python SDK

      import sagemaker # Initialize SageMaker Session object so you can interact with AWS resources sess = sagemaker.Session() # Get the role ARN role = sagemaker.get_execution_role() print(role) >> arn:aws:iam::<your-aws-account-id>:role/<your-role-name>

      For more information about using the SageMaker Python SDK, see the SageMaker Python SDK API.

    • Using the AWS Identity and Access Management (IAM) console

      Navigate to the IAM console at In the IAM Resources section, choose Roles to view a list of roles in your AWS account. Select or create a role that has AmazonSageMakerFullAccess, AWSIoTFullAccess, and AmazonS3FullAccess.

      For more information on IAM, see What is IAM?

  4. Have an S3 bucket URI.

    You need to have at least one Amazon Simple Storage Service (Amazon S3) bucket URI to store your Neo-compiled model, the output of the Edge Manager packaging job, and sample data from your device fleet.

    Use one of the following methods to create an Amazon S3 bucket:

    • Programmatically with the SageMaker Python SDK

      You can use the default Amazon S3 bucket during a session. A default bucket is created based on the following format: sagemaker-{region}-{aws-account-id}. To create a default bucket with the SageMaker Python SDK, use the following:

      import sagemaker session=sagemaker.create_session() bucket=session.default_bucket()
    • Using the Amazon S3 console

      Open the Amazon S3 console at and see How do I create an S3 Bucket? for step-by-step instructions.