Details on the miniwdl engine deployed by Amazon Genomics CLI


miniwdl is free open source software distributed under the MIT licence developed by the Chan Zuckerberg Initiative.

The source code for miniwdl is available on GitHub. When deployed with Amazon Genomics CLI miniwdl makes use of the miniwdl-aws extension which is also distributed under the MIT licence.


There are four components of a miniwdl engine as deployed in an Amazon Genomics CLI context:

Image of infrastructure deployed in a miniwdl context

WES Adapter

Amazon Genomics CLI communicates with the miniwdl engine via a GA4GH WES REST service. The WES Adapter implements the WES standard and translates WES calls into calls to the miniwdl head process.

Head Compute Environment

For every workflow submitted, the WES adapter will create a new AWS Batch Job that contains the miniwdl process responsible for running that workflow. These miniwdl “head” jobs are run in an “On-demand” AWS Fargate compute environment even when the actual workflow tasks run in a Spot environment. This is to prevent Spot interruptions from terminating the workflow coordinator.

Task Compute Environment

Workflow tasks are submitted by the miniwdl head job to an AWS Batch queue and run in containers using an AWS Compute Environment. Container characteristics are defined by the resources requested in the workflow configuration. AWS Batch coordinates the elastic provisioning of EC2 instances (container hosts) based on the available work in the queue. Batch will place containers on container hosts as space allows.

Session Cache and Input Localization

Any context with a miniwdl engine will use an Amazon Elastic File System (EFS) volume as scratch space. Inputs from S3 are localized to the volume by jobs that the miniwdl engine spawns to copy these files to the volume. Outputs are copied back to S3 using a similar process. Workflow tasks access the EFS volume to obtain inputs and write intermediates and outputs.

The EFS volume is used by all miniwdl engine “head” jobs to store metadata necessary for call caching.

The EFS volume will remain in your account for the lifetime of the context and are destroyed when contexts are destroyed. Because the volume will grow in size as you run more workflows we recommend destroying the context when done to avoid on going EFS charges.

Using miniwdl as a Context Engine

You may declare miniwdl to be the engine for any contexts wdl type engine. For example:

    requestSpotInstances: false
      - type: wdl
        engine: miniwdl

Call Caching

Call caching is enabled by default for miniwdl and because the metadata is stored in the contexts EFS volume call caching will work across different engine “head” jobs.

To disable call caching you can provide the --no-cache engine option. You may do this in a workflows MANIFEST.json by adding the following key/ value pair.

  "engineOptions": "--no-cache"