1. Edge-based Machine Learning Inference with AWS IoT Greengrass

    Python

    To set up edge-based machine learning inference using AWS IoT Greengrass, we're going to create a Pulumi program that performs the following:

    1. Configures domain settings for AWS IoT with aws.iot.DomainConfiguration.
    2. Deploys a machine learning model to a fleet of edge devices using AWS SageMaker's DeviceFleet.
    3. Defines a Greengrass deployment that will contain the components for our machine learning inference on edge devices with aws-native.greengrassv2.Deployment.

    Here's what each step accomplishes:

    • IoT Domain Configuration: It is necessary to configure the domain for AWS IoT so that devices can communicate securely. We set up custom domain settings to enable this communication, which may include server certificates and other TLS configurations.

    • SageMaker's Device Fleet: SageMaker allows you to manage a fleet of devices that can be trained and used for inference. By registering our devices with SageMaker, we can then deploy machine learning models directly to our fleet for local inferencing. This step involves setting up the device fleet with the necessary configurations like the output storage location for model artifacts and the IAM role for permissions.

    • Greengrass Deployment: AWS IoT Greengrass lets us run local compute, messaging, data caching, sync, and ML inference capabilities on connected devices in a secure way. By creating a Greengrass deployment, we can deploy the software that will execute the machine learning model on the edge device. This requires defining the target devices (the ARN of the Greengrass core device or group) as well as the components that will be deployed.

    Let's start building the Pulumi program:

    import pulumi import pulumi_aws as aws import pulumi_aws_native as aws_native # Before we begin, ensure you have AWS configured with the necessary credentials # and permissions to create these resources. # Step 1: Set up domain configuration for AWS IoT. domain_config = aws.iot.DomainConfiguration("myIotDomainConfig", # Substitute these values with your actual domain configuration. domain_name="myIotDomain.example.com", server_certificate_arns=["arn:aws:acm:region:account-id:certificate/certificate-id"], validation_certificate_arn="arn:aws:acm:region:account-id:certificate/validation-certificate-id", service_type="DATA", tags={ "Name": "MyIotDomain" } ) # Step 2: Create a SageMaker device fleet for ML inferencing on the edge. device_fleet = aws.sagemaker.DeviceFleet("myDeviceFleet", device_fleet_name="my-device-fleet", role_arn="arn:aws:iam::account-id:role/service-role/role-name", output_config={ "s3_output_location": "s3://my-sagemaker-models-bucket/output", "kms_key_id": "arn:aws:kms:region:account-id:key/key-id" # Optional KMS key for encryption }, # The following parameter is set to 'True' to allow the devices to assume # an IoT role alias. enable_iot_role_alias=True ) # Step 3: Create a Greengrass V2 deployment to send the inference to the device. greengrass_deployment = aws_native.greengrassv2.Deployment("myGreengrassDeployment", target_arn="arn:aws:iot:region:account-id:thing/thing-name", # Substitute with your Greengrass core device ARN. deployment_name="my-ml-inference-deployment", components={ # Here you should define the components necessary for your ML inference. # For simplicity, this example assumes you have created a custom component # that contains your ML model which is named 'MyMLModelComponent'. "MyMLModelComponent": { "componentVersion": "1.0.0", "configurationUpdate": { "merge": "{\"MLModelConfiguration\": \"Initial configuration\"}" } }, }, deployment_policies={ "failure_handling_policy": "ROLLBACK", # Specify timeout and update policies as per your requirements. "component_update_policy": { "action": "NOTIFY_COMPONENTS", "timeout_in_seconds": 60 }, "configuration_validation_policy": { "timeout_in_seconds": 30 } }, # The IoT job configuration is optional and can be customized. iot_job_configuration={} ) # Export the ARNs of the created resources so that they can be referenced later. pulumi.export("iot_domain_config_arn", domain_config.arn) pulumi.export("sagemaker_device_fleet_arn", device_fleet.arn) pulumi.export("greengrass_deployment_id", greengrass_deployment.deployment_id)

    To run this program, you would need to have Pulumi CLI installed and AWS correctly configured for your account with the required permissions. Then simply run pulumi up in the directory where this code is. After confirming the changes, Pulumi will provision these AWS resources and output the ARNs for further reference.

    In this program, you should replace placeholder values with actual values applicable to your AWS setup. Also, the component 'MyMLModelComponent' is a fictional example; you'll need to use the actual name and version of the Greengrass component you've prepared with your machine learning model.

    You can find more details about each used resource in the program in their respective Pulumi's documentations:


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