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Introduction to CDK for Research Software Engineers

Why do I care?

Science depends on the reproducibility of results. This includes the reproducibility of analysis.

Historically, a lot of researchers struggled to run each other's code; developed on an individual specific system, and requiring difficult porting work to run elsewhere.

Containers solve that don't they?

Containers help solve a lot of this problem by at least packaging an application and a lot of its dependencies in a single runnable module. But that's only part of the story. What about the dependencies which aren't directly part of the application? What about everything else you need to run a scientific analysis at scale? Batch queues. HPC nodes. High performance networks. Firewalls. Centralised logging. Fault notifications. The list goes on...

AWS has all of these features and more, but configuring them all individually through the AWS console can be laborious and error prone.

AWS CloudFormation is a service which allows you to describe all of your infrastratucture in template files, in either YAML or JSON. The service turns these templates into a series of API calls and executes them to created the infrastructure.

However, CloudFormation templates still require you to specify many details and can become unwieldy to write and debug, especially as your stack gets more complex and has many cross-references between resources.

What if you could write code in Python, or other languages with which you are already familiar, and have that generate the CloudFormation template for you? And apply sensible, best practice defaults so that you don't have to directly code every last detail? That's what the AWS Cloud Development Kit does.

This allows you to specify your entire application; the containerised app itself, and the entire infrastructure to run it reproducibly, in a single code repository.

This is an example of a simple research-oriented architecture, written almost entirely in Python, to illustrate the principles.

Architecture diagram

  1. Dropping files in the Input Bucket triggers the Bucket Arrival lambda function
  2. The function submits a job to the Batch Queue which automatically spins up instance(s) to process the jobs.
  3. CDK uses docker on your machine to build and upload the container to the Amazon Elastic Container registry. AWS Batch uses this container to process the file in the input bucket.
  4. The results of the job are then stored in Output Bucket
  5. An Event Bus watches the AWS Batch events, and filters them for job status changes to SUCCEEDED or FAILED and sends these events to the JobCompletion SNS topic.
  6. Creates a budget which warns you if your spend goes over an amount you specify, and inactivates the queue so no more work can be submitted.

Prerequisites

  1. Install Node.js 14.15.0 or later on your system
  2. Install Docker on your system
  3. Install CDKv2: npm install -g aws-cdk
  4. Install Python 3.7 or higher on your system, including pip and virtualenv
  5. Recommended: Install the AWS CLI on your system
  6. Clone this repository
  7. Navigate to the root of your checkout
  8. Install a virtualenv: python3 -m venv .venv
  9. Activate the virtualenv: source .venv/bin/activate on MacOS/Linux, or .venv\Scripts\activate.bat on Windows
  10. Install the python modules needed: pip install -r requirements.txt
  11. If you've never used CDK before, run cdk bootstrap
  12. In order for the budget to work correctly, you need to have access to your billing information, and activate the cost allocation tag research-stack-id . Note this can take up to 24 hours to take effect, and resources created before the activation will not be counted in the budget.

Deploy the stack

cdk deploy --parameters NotificationEmail=youraddress@example.com --parameters BudgetLimit=100

Make a note of the name of the input and output buckets that were created.

Check your email - SNS will ask you to confirm your email address for the notifications to be sent to, if you have never had emails from SES before.

Submitting work to the stack

Use the aws CLI or web interface to add files to the OutputBucket :

echo hello research world | aws s3 cp - s3://researchcdkexamplestack-inputbucketnnnnnnnn-mmmmmmmm/helloworld.txt

Watch the magic happen in the AWS Console, by navigating to AWS Batch and looking at Jobs.

Download your results from the output bucket:

aws s3 cp s3://researchcdkexamplestackxxxxxxxx-yyyyyyyy/helloworld.txt.out -

Before you start

This project is set up like a standard Python project. You need to create a virtualenv for this to work.

To create the virtualenv it assumes that there is a python3 (or python for Windows) executable in your path with access to the venv package

To manually create a virtualenv on MacOS and Linux:

python3 -m venv .venv

After the init process completes and the virtualenv is created, you can use the following step to activate your virtualenv.

source .venv/bin/activate

If you are on a Windows platform, you would activate the virtualenv like this:

% .venv\Scripts\activate.bat

Once the virtualenv is activated, you can install the required dependencies.

pip install -r requirements.txt

If you have never used CDK before on your account or in the region you're about to deploy to, you need to bootstrap CDK in your account and region:

cdk bootstrap

At this point you can now synthesize the CloudFormation template for this code.

cdk synth

To add additional dependencies, for example other CDK libraries, just add them to your setup.py file and rerun the pip install -r requirements.txt command.

Useful commands

  • cdk ls list all stacks in the app
  • cdk synth emits the synthesized CloudFormation template
  • cdk deploy deploy this stack to your default AWS account/region
  • cdk diff compare deployed stack with current state
  • cdk docs open CDK documentation

Enjoy!