Genomics Workflows on AWS

This repository is the source code for Genomics Workflows on AWS. It contains markdown documents that are used to build the site as well as source code (CloudFormation templates, scripts, etc) that can be used to deploy AWS infrastructure for running genomics workflows.

If you want to get the latest version of these solutions up and running quickly, it is recommended that you deploy stacks using the launch buttons available via the hosted guide.

If you want to customize these solutions, you can create your own distribution using the instructions below.

Creating your own distribution

Clone the repo

git clone https://github.com/aws-samples/aws-genomics-workflows.git

Create an S3 bucket in your AWS account to use for the distribution deployment

aws s3 mb <dist-bucketname>

Create and deploy a distribution from source

cd aws-genomics-workflows
bash _scripts/deploy.sh --deploy-region <region> --asset-profile <profile-name> --asset-bucket s3://<dist-bucketname> test

This will create a dist folder in the root of the project with subfolders dist/artifacts and dist/templates that will be uploaded to the S3 bucket you created above.

Use --asset-profile option to specify an AWS profile to use to make the deployment.

Note: the region set for --deploy-region should match the region the bucket <dist-bucketname> is created in.

You can now use your deployed distribution to launch stacks using the AWS CLI. For example, to launch the GWFCore stack:

TEMPLATE_ROOT_URL=https://<dist-bucketname>.s3-<region>.amazonaws.com/test/templates

aws cloudformation create-stack \
    --region <region> \
    --stack-name <stackname> \
    --template-url $TEMPLATE_ROOT_URL/gwfcore/gwfcore-root.template.yaml \
    --capabilities CAPABILITY_IAM CAPABILITY_AUTO_EXPAND \
    --parameters \
        ParameterKey=VpcId,ParameterValue=<vpc-id> \
        ParameterKey=SubnetIds,ParameterValue=\"<subnet-id-1>,<subnet-id-2>,...\" \
        ParameterKey=ArtifactBucketName,ParameterValue=<dist-bucketname> \
        ParameterKey=TemplateRootUrl,ParameterValue=$TEMPLATE_ROOT_URL \
        ParameterKey=S3BucketName,ParameterValue=<store-buketname> \
        ParameterKey=ExistingBucket,ParameterValue=false

Building the documentation

The documentation is built using mkdocs.

Install dependencies:

$ conda env create --file environment.yaml

This will create a conda environment called mkdocs

Build the docs:

$ conda activate mkdocs
$ mkdocs build

License Summary

This sample code is made available under a modified MIT license. See the LICENSE file.