Overview: Running workflows in Terra

Allie Hajian
  • Updated

An introduction to workflows - one of two analysis modes you can use on the Terra platform. Workflows (aka pipelines) are a series of steps performed by an external compute engine - often used for automated, bulk analysis (such as aligning genomic reads).

Overview: Running a workflow in a Terra workspace

Workflows run on Terra are written in Workflow Description Language (WDL), a workflow processing language that is easy for humans to read and write. Running a workflow (also called a pipeline) in a Terra workspace requires the following. 

"Can compute" access to the workspace  

You need permission to do any operations that have a Google Cloud cost (i.e., run workflows) in a workspace. You can do this if someone shares a workspace with you with "Can-Compute Writer" privileges. If you create or copy a workspace using your own Billing project, you own the workspace and can run workflows on it by default. 

One or more workflows

If you clone a workspace that already contains workflows (see Featured Workspaces in the Terra Library), these tools will be in your copy as well. If the Workflows page is empty, you can import workflows from the Terra library (code and workflows section), Dockstore, or the Broad Methods Repository.

Input data

Input data files can be located in the workspace's storage bucket (Google bucket) or an external bucket. The workflow can pull the data in these files into its virtual machine (VM) using file URLs that are stored in the workspace's data tables or specified when configuring the workflow.

What happens when you run a workflow in Terra?

A workflow in its simplest form is a task consisting of

  • Path(s) of input files to read from Cloud Storage.
  • A Docker image to run.
  • Commands (the workflow) to run in the Docker image.
  • Cloud resources to use (number of CPUs, amount of memory, disk size and type).
  • Path(s) of output files/directories to write to Cloud Storage.

To run a workflow in Terra, you will

  • Specify the input files' paths from Cloud Storage.
  • Specify runtime options, including the Docker image.
  • Submit the workflow to Terra.

Behind the scenes, Terra takes care of the details

  • Terra sends the built-in Cromwell server a packet of information containing the workflow code and inputs.
  • Cromwell - a Workflow Management System geared towards scientific workflows - parses the workflow and starts dispatching individual jobs to PAPI (the Pipelines API).
  • PAPI executes the tasks on the Google Compute Engine (GCE) and writes the output to the Workspace bucket.

The Pipelines API will

  • Create a Compute Engine virtual machine (VM).
  • Download the Docker image.
  • Download the input files.
  • Run a new Docker container with the specified image and workflow commands.
  • Upload the output files.
  • Destroy the VM.

Diagram schematizing what happens when you run a workflow on Terra. A green hexagon represents an individual workspace in Terra. Within this workspace are the workflow's inputs, commands, and data. Arrows indicate these the workflow's commands can come from Dockstore, the Broad Methods Repository, or the Terra Library. When a workflow is run, it is sent to Terra's exercution engine, Cromwell. Cromwell then starts individual jobs on a virtual machine using Google's Pipelines API (PAPI). Data for these jobs are pulled from the Terra workspace, or from an external Google Cloud bucket. If any jobs output new data, those outputs are writter back to the Terra workspace.
Overview of workflow submission in Terra from Genomics in the Cloud 
(by Geraldine A. Van der Auwera and Brian D. O'Connor - O'Reilly Press)

Cromwell and PAPI defined Cromwell is an open source (BSD 3-clause) execution engine written in Java that supports running WDL on three types of platform: a local machine (e.g., your laptop), a local cluster/compute farm accessed via a job scheduler (e.g., GridEngine), or a cloud platform (e.g., Google Cloud or Amazon AWS).

Pipelines API (aka "PAPI") is a Google Cloud service that provides an easy way to launch and monitor tasks running in the cloud.

Practice pipelining with the Workflows Quickstart

One way to get up and running quickly is to clone and run the workflows in a Featured Workspace.

To learn the basics of running a workflow in Terra, the Workflows Quickstart is a self-guided tutorial that includes everything you need to get hands-on experience running workflows. It's the second in a series of three Quickstarts that walk through a mock study of the correlation between height and grades for a cohort of 7th, 8th, and 9th graders.

Hands-on workflows practice

You will first run a preconfigured workflow, then set up and run the same workflow from a blank configuration card. As a bonus, you can run a follow-up third workflow to analyze data generated by the first exercises.

Tutorial workspace | Step-by-step guide

To get started, clone the T101 Workflows Quickstart workspace to your own billing account and work through the three exercises listed below.

Workflows quickstart flowDiagram showing the three main sections in the T101 Workflows Quickstart tutorial. Section 1 demonstrates how to run a pre-configured workflow using a data table called 'student.' Section 2 demonstrates how to configure and run a workflow using the same data table. Section 3 is a bonus section that demonstrates how to run a follow-up workflow on the data output by sections 1 and 2.

Three steps to complete the workflows quickstart

  1. Calculate the students' average GPA by running a pre-configured workflow on data in the student table.
  2. Calculate the students' average GPA by setting up and running a workflow from scratch on data in the student table.
  3. (optional bonus) Calculate the class average GPA by setting up and running a workflow on generated data from part 2.

The step-by-step guide walks through these exercises in greater detail.

Was this article helpful?

2 out of 3 found this helpful

Comments

0 comments

Please sign in to leave a comment.