deepracer-model-creation-task

This project is an AWS serverless application that automatically creates an AWS DeepRacer Model from the model files prepared by the DeepRacer-For-Cloud local training.

The entry point of the application is the S3 upload event of hyperparameters.json file in the S3 upload bucket of the DeepRacer-For-Cloud setup. The event invokes the Lambda function.

The application is built and deployed with the SAM CLI.

The project includes the following files and folders.

• app.py - Code of the application's Lambda function.
• Dockerfile - A Docker file of the Lambda function packaged as Image.
• requirements.txt - The Python3 dependencies.
• template.yaml - A SAM template that defines the application's AWS resources.

The application uses several AWS resources that are defined in the template.yaml file in this project. You can update the template to add AWS resources through the same deployment process that updates your application code.

AWS DeepRacer

AWS DeepRacer is an AWS-managed service for studying the basics of Reinforcement Learning (one of the Machine Learning types) in a gamification mode.

deepracer-utils

The Lambda function code uses deepracer-utils Python library from AWS Deepracer Community.

The library provides a set of utilities including a boto3 enhancement that allows to create a client for AWS DeepRacer service.

Deploy the sample application

The Serverless Application Model Command Line Interface (SAM CLI) is an extension of the AWS CLI that adds functionality for building and testing Lambda applications. It uses Docker to run your functions in an Amazon Linux environment that matches Lambda. It can also emulate your application's build environment and API.

To use the SAM CLI, you need the following tools.

• SAM CLI - Install the SAM CLI
• Docker - Install Docker community edition

You may need the following for local testing.

• Python 3 installed

To build and deploy your application for the first time, run the following in your shell:

sam build
sam deploy --guided

The first command will build a docker image from a Dockerfile and then copy the source of your application inside the Docker image. The second command will package and deploy your application to AWS, with a series of prompts:

• Stack Name: The name of the stack to deploy to CloudFormation. This should be unique to your account and region, and a good starting point would be something matching your project name.
• AWS Region: The AWS region you want to deploy your app to.
• Confirm changes before deploy: If set to yes, any change sets will be shown to you before execution for manual review. If set to no, the AWS SAM CLI will automatically deploy application changes.
• Allow SAM CLI IAM role creation: Many AWS SAM templates, including this example, create AWS IAM roles required for the AWS Lambda function(s) included to access AWS services. By default, these are scoped down to minimum required permissions. To deploy an AWS CloudFormation stack which creates or modifies IAM roles, the CAPABILITY_IAM value for capabilities must be provided. If permission isn't provided through this prompt, to deploy this example you must explicitly pass --capabilities CAPABILITY_IAM to the sam deploy command.
• Save arguments to samconfig.toml: If set to yes, your choices will be saved to a configuration file inside the project, so that in the future you can just re-run sam deploy without parameters to deploy changes to your application.

Use the SAM CLI to build and test locally

Build your application with the sam build command.

deepracer-model-creation-task$ sam build

The SAM CLI builds a docker image from a Dockerfile and then installs dependencies defined in requirements.txt inside the docker image. The processed template file is saved in the .aws-sam/build folder.

Add a resource to your application

The application template uses AWS Serverless Application Model (AWS SAM) to define application resources. AWS SAM is an extension of AWS CloudFormation with a simpler syntax for configuring common serverless application resources such as functions, triggers, and APIs. For resources not included in the SAM specification, you can use standard AWS CloudFormation resource types.

Fetch, tail, and filter Lambda function logs

To simplify troubleshooting, SAM CLI has a command called sam logs. sam logs lets you fetch logs generated by your deployed Lambda function from the command line. In addition to printing the logs on the terminal, this command has several nifty features to help you quickly find the bug.

NOTE: This command works for all AWS Lambda functions; not just the ones you deploy using SAM.

deepracer-model-creation-task$ sam logs -n ImportModelFunction --stack-name deepracer-model-creation-task --tail

You can find more information and examples about filtering Lambda function logs in the SAM CLI Documentation.

Cleanup

To delete the sample application that you created, use the AWS CLI. Assuming you used your project name for the stack name, you can run the following:

aws cloudformation delete-stack --stack-name deepracer-model-creation-task

Alternatively, you can use SAM CLI delete command.

sam delete

Resources

See the AWS SAM developer guide for an introduction to SAM specification, the SAM CLI, and serverless application concepts.

Next, you can use AWS Serverless Application Repository to deploy ready to use Apps that go beyond hello world samples and learn how authors developed their applications: AWS Serverless Application Repository main page