Deploy the Model Package Directly with SageMaker Edge Manager Deployment API

SageMaker Edge Manager provides a deployment API that you can use to deploy models to device targets without AWS IoT Greengrass. It is useful in situations where you want to update models independently of firmware updates or application deployment mechanisms. You can use the API to integrate your edge deployments into a CI/CD workflow to automatically deploy models once you have validated your model for accuracy. The API also has convenient rollback and staged rollout options for you to ensure models work well in a particular environment before wider rollout.

To use the Edge Manager deployment API first compile and package your model. For information on how to compile and package your model, see Prepare Your Model for Deployment. The following sections of this guide show how you can create edge deployments using SageMaker API, after you have compiled and packaged your models.

Create an edge deployment plan

You can create an edge deployment plan with the CreateEdgeDeploymentPlan API. The deployment plan can have multiple stages. You can configure each stage to rollout the deployment to a subset of edge devices (by percentage, or by device name). You can also configure how rollout failures are handled at each stage.

The following code snippet shows how you can create an edge deployment plan with 1 stage to deploy a compiled and package model to 2 specific edge devices:

import boto3

client = boto3.client("sagemaker")

client.create_edge_deployment_plan(
    EdgeDeploymentPlanName="edge-deployment-plan-name",
    DeviceFleetName="device-fleet-name",
    ModelConfigs=[
        {
            "EdgePackagingJobName": "edge-packaging-job-name",
            "ModelHandle": "model-handle"
        }
    ],
    Stages=[
        {
            "StageName": "stage-name",
            "DeviceSelectionConfig": {
                "DeviceSubsetType": "SELECTION",
                "DeviceNames": ["device-name-1", "device-name-2"]
            },
            "DeploymentConfig": {
                "FailureHandlingPolicy": "ROLLBACK_ON_FAILURE"
            }
        }
    ]
)
	    

Instead of specific devices, if you want to deploy to the model to a percentage of devices in your fleet, then set the value of DeviceSubsetType to "PERCENTAGE" and replace "DeviceNames": ["device-name-1", "device-name-2"] with "Percentage": desired-percentage in the above example.

Stages can be added after the deployment plan has been created with the CreateEdgeDeploymentStage API, in case you want to start rolling out new stages after validating your test rollout success. For more information about deployment stages see DeploymentStage.

Start the edge deployment

After creating the deployment plan and the deployment stages, you can start the deployment with the StartEdgeDeploymentStage API.

client.start_edge_deployment_stage(
    EdgeDeploymentPlanName="edge-deployment-plan-name",
    StageName="stage-name"
)
	    

Check the status of the deployment

You can check the status of the edge deployment with the DescribeEdgeDeploymentPlan API.

client.describe_edge_deployment_plan(
    EdgeDeploymentPlanName="edge-deployment-plan-name"
)