Device

Learn how to create, update, and manage AWS SageMaker Devices using Alchemy Cloud Control.

The Device resource allows you to manage AWS SageMaker Devices for deploying machine learning models on edge devices.

Minimal Example

Create a basic SageMaker Device associated with a device fleet:

import AWS from "alchemy/aws/control";

const basicDevice = await AWS.SageMaker.Device("basicDevice", {
  DeviceFleetName: "myDeviceFleet",
  Device: {
    DeviceName: "myEdgeDevice",
    DeviceType: "RaspberryPi"
  },
  Tags: [
    { Key: "Project", Value: "MLEdge" },
    { Key: "Environment", Value: "Production" }
  ]
});

Advanced Configuration

Configure a SageMaker Device with additional properties like tags and adopt existing resource option:

const advancedDevice = await AWS.SageMaker.Device("advancedDevice", {
  DeviceFleetName: "myDeviceFleet",
  Device: {
    DeviceName: "advancedEdgeDevice",
    DeviceType: "JetsonNano"
  },
  Tags: [
    { Key: "Project", Value: "MLEdge" },
    { Key: "Environment", Value: "Staging" }
  ],
  adopt: true // adopt existing resource if it exists
});

Using Existing Devices

If you want to adopt an existing device without creating a new one, you can set the adopt property to true:

const existingDevice = await AWS.SageMaker.Device("existingDevice", {
  DeviceFleetName: "myDeviceFleet",
  Device: {
    DeviceName: "existingDevice",
    DeviceType: "RaspberryPi"
  },
  adopt: true
});

Setting Tags

You can specify tags to categorize your devices effectively, which can be useful for organization and billing:

const taggedDevice = await AWS.SageMaker.Device("taggedDevice", {
  DeviceFleetName: "myDeviceFleet",
  Device: {
    DeviceName: "taggedEdgeDevice",
    DeviceType: "JetsonTX2"
  },
  Tags: [
    { Key: "Owner", Value: "DataScienceTeam" },
    { Key: "Purpose", Value: "ModelInference" }
  ]
});