This step-by-step tutorial will help guide you through extending a model with AWS DeepLens. AWS DeepLens is a fully programmable video camera that comes with tutorials, code, and pre-trained models designed to expand deep learning skills.
In this tutorial, you will capture the events from your AWS DeepLens model and put them in a queue ready for further processing. There are many use cases to extend the output AWS DeepLens models utilizing AWS Lambda functions, such as sending alerts, opening doors, populating analytical dashboards, to name just a few (see step 3.c. in this tutorial).
You will need an AWS DeepLens device in order to complete this tutorial. You can pre-order now on amazon.com.
If this is your first time using your AWS DeepLens check out the How to Configure AWS DeepLens and the Creating and Deploying an AWS DeepLens Project tutorials to help you get started.
Open the AWS Management Console in a new browser window, so you can keep this step-by-step guide open. When the screen loads, enter your user name and password to get started. Then type DeepLens in the search bar and select AWS DeepLens to open the console.
In this step, you will build upon the new project created in the tutorial Creating and Deploying an AWS DeepLens Project and view the output.
d. In the next few steps we will use the AWS IoT console. Follow this link or select the AWS icon on the top left and search IoT in the list of AWS services.
f. Verify that you see some messages from the device.
Note: You can also verify that your model is running by opening the output stream as described in the Creating and Deploying an AWS DeepLens Project tutorial.
In this step, you will use the AWS IoT console, create a rule to send messages to an SQS queue ready for polling.
g. To secure who can write and read from the new queue you need to create an IAM role. Select Create a new role.
Unfamiliar with IAM roles? Learn more here.
In this final step, you will learn how to view the messages in the SQS console.
a. Follow this link or select the AWS icon on the top left and search for SQS from the AWS services list.
You have created your first deep learning computer vision project!
You can now play around the other project templates, try extending your project functionality with AWS Lambda or train a deep learning model using Amazon SageMaker.
Now that you have deployed a model to AWS DeepLens, pick one of the following options to further expand your knowledge.
Dive deeper
Get even more info on how to use AWS DeepLens. Check out the full Getting Started Guide »
Learn more
Learn how to customize and display project output from AWS DeepLens on an HTML page. Read the blog here »
Build a project
Get an overview of training a model with Amazon SageMaker and extending it with AWS Lambda. Read the blog here»