LTI Example Tool

This is an LTI tool allow an instructor to create an assessment, and allow students to complete the assessment and get a grade

Prerequisites

• AWS Command Line Interface
• AWS CDK
• Python 3.9 or later
• Poetry - More info on working with Poetry can be found here

To create a virtualenv on MacOS and Linux:

$ poetry install

Drop into the python virtual env

$ poetry shell

Deployment

If you haven't already be sure to bootstrap your cdk environment. This only needs to be done once.

$ cdk bootstrap 

At this point you can now deploy the stacks to your AWS account

workshop-application-stack

This stack deploys the LTI application directly to you AWS account.

$ cdk deploy 

pipeline-stack (optional)

This stack sets up a CI/CD pipeline connected to a GitHub repository which will build and deploy the LTI application when new code is checked in.

To deploy the pipeline issue the following command

$ cdk deploy -c account=$AWS_DEFAULT_ACCOUNT -c region=$AWS_DEFAULT_REGION -c repo=<THE_GITHUB_ORG>/<THE_GIT_HUB_REPOSITORY> -c branch=$(git rev-parse --abbrev-ref HEAD) -c codestar_connection_arn=<CODESTART_CONNECTION_ARN> pipeline-stack-$(git rev-parse --abbrev-ref HEAD | sed -e 's/\//-/g')

To complete the pipeline setup you must go into the AWS Console, navigate to CodePipeline, on the left hand side expand "Settings" and click "Connections"

Look for a connection with your repo name and a status of "Pending", click on this connection

Click the button that says Update pending connection

Click "Install a new app"

Select the correct organization

Ensure the right repository is selected, hit save (you may have to toggle the radio buttons to get the save button to activate)

Finally, click "Connect" to complete the connection setup

Once the connection to github is complete you can trigger the pipeline by checking code into the configured branch or manually triggering the pipeline by clicking the "Release change" button in the AWS Console

You should deploy the pipeline stack first before

Run locally with Flask

You can also run locally using the following command

$ flask run

To update things if a dependency has changed run

poetry update

Configuration

If you want to run locally via flask run you'll need to set some environment variables. It's best to create a .env (or .envrc if using direnv) file. These are the possible values you can change:

export AWS_PROFILE=879999_Developer
export AWS_DEFAULT_ACCOUNT=879999
export AWS_DEFAULT_REGION=us-east-1
export AWS_REGION=us-east-1
export TABLE_NAME=your_table_name
export LOCAL=true
export PORT=5000
export DOMAIN=127.0.0.1
export KMS_KEY_ID='arn:aws:kms:???'
export KMS_SYMMETRIC_KEY_ID='arn:aws:kms:???'

# The next three are stored in ParameterStore. Create keys for your instance
export LTI_TOOLING_API_URL_KEY='/keys/somewhere/tool_url'
export LEARN_APPLICATION_KEY_KEY='/keys/somewhere/learn_key'
export LEARN_APPLICATION_SECRET_KEY='/keys/somewhere/learn_secret'```

### MKDocs

We suggest the use of [Mkdocs](https://www.mkdocs.org/getting-started/) for documentation.

#### Install

pip install mkdocs

#### Serve

mkdocs serve

#### Pre-commit hooks

This project has a set of hooks for [pre-commit](https://pre-commit.com/) for formatting.

To install this hooks into your repo, run.

pre-commit install