AWS Greengrass Samples
License Summary
These samples are made available under a modified MIT license. See the LICENSE file.
greengrass-dependency-checker
This folder contains tools that help you check for system-level dependencies that Greengrass requires to be run. Refer to the requirements outlined in the Greengrass Documentation, as well as the Greengrass Getting Started Guide, Module 1: http://docs.aws.amazon.com/greengrass/latest/developerguide/gg-gs.html
hello-world-python
This folder contains a sample Lambda function that uses the Greengrass SDK to publish a HelloWorld message to AWS IoT. Refer to the Greengrass Getting Started Guide, Module 3 (Part I): http://docs.aws.amazon.com/greengrass/latest/developerguide/gg-gs.html
hello-world-counter-python
This folder contains a sample Lambda function that uses the Greengrass SDK to publish HelloWorld messages to AWS IoT, maintaining state. Refer to the Greengrass Getting Started Guide, Module 3 (Part II): http://docs.aws.amazon.com/greengrass/latest/developerguide/gg-gs.html
traffic-light-example-python
This folder contains a set of functions that demonstrate a traffic light example using two Greengrass devices, a light controller and a traffic light. It also contains a Lambda function that collects data from the traffic light system and sends it to an AWS DynamoDB table. Refer to the Greengrass Getting Started Guide, Modules 5 and 6: http://docs.aws.amazon.com/greengrass/latest/developerguide/gg-gs.html
greengrass-opcua-adapter-nodejs
This folder contains a sample NodeJS Lambda function that will that will connect to a preconfigured list of OPC-UA servers, and monitored configured NodeIds for change. If a monitor node's value changes, this lambda get notified, and republishes that value change onto a specific topic. Refer to this documentation page for more details: http://docs.aws.amazon.com/greengrass/latest/developerguide/opcua.html
ml-at-edge-examples
This folder contains the machine learning resources. It includes pre-built libraries for MxNet and Tensorflow on three edge devicesd: RaspBerry Pi2, Nvidia Jetson TX2 and AWS DeepLens. It also includes examples for machine learning inference on these devices.