Use cases#

plantit aims to support two user groups with different concerns and priorities.

  • researchers: analyzing data, running models & simulations (submitting workflows)

  • developers: publishing and maintaining research software (publishing workflows)

With plantit the latter group can quickly and easily publish an algorithm to a broader, possibly non-technical user community. In this way it’s a continuous deployment tool. It’s also a science gateway, hosting plug-and-play algorithms in a web GUI such that any user can leverage XSEDE HPC/HTC clusters for high-throughput phenomics, no programming experience required.

Conceptual model#

plantit has a few fundamental abstractions:

  • Dataset

  • Agent

  • Workflow

  • Task

A Dataset is a set of data objects. A Workflow is a containerized research application. A workflow must yield a dataset as output and may accept one as input (workflows should be designed as functions or generators, not for their side effects — ideally, they should have none). An instantiation of a workflow is called a Task. An Agent is a cluster queue that can run tasks.


A Dataset is a collection of data objects in the CyVerse data store.


An Agent is a deployment target: an abstraction of a cluster or supercomputer along with SLURM scheduler configuration details.


A Workflow is an executable research application packaged into a Docker image. Workflows are deployed in the Singularity container runtime. To define a workflow, add a plantit.yaml file to any public GitHub repository.


A Task is an instance of a workflow, deployed to an agent. When a task is submitted from the browser, the plantit web app hands it to an internal queue feeding an orchestrator process. When the orchestrator picks up the task, it generates a job script and submits it to the selected cluster/supercomputer scheduler, then monitors its progress until completion.

../../_images/cycle.jpgTask Overview

The task lifecycle is a state machine progressing from CREATED to RUNNING to one of several mutually exclusive final states (COMPLETED, FAILED, TIMEOUT, or CANCELLED).

../../_images/lifecycle.jpgTask Lifecycle


A Project is a MIAPPE investigation, which may contain one or more studies. MIAPPE (Minimum Information About a Plant Phenotyping Experiment) is a formal ontology for organizing data, metadata, experiments, and analyses. plantit allows datasets and tasks to be freely associated with MIAPPE projects.

Submitting tasks#

Select a workflow#

To explore workflows, navigate to the Workflows tab from the home view.


By default, this page will display the Featured workflow context: a curated set of applications provided by the Computational Plant Science lab, collaborators, and other researchers.

Click the Featured dropdown to select a different context. Options include:

  • Examples: a small set of simple workflows to serve as templates and examples

  • Public: all publicly available workflows

  • Yours: your own workflows (private and public)

  • [Organization]: workflows belonging to a particular organization

  • [Project]: workflows associated with a particular MIAPPE project

Select a workflow to view its authorship, related publications, parameter list, and deployment configuration.

Submit to an agent#

../../_images/workflow_info.pngTask information

To configure and submit a task for the workflow you’ve selected, click Submit. This will present some configuration options including (at least):

  • ID: the task’s (unique) identifier

  • Tags: arbitrary text tags to associate with the task

  • Time: the task’s time limit

  • Agent: the agent to submit the task to

  • Output: the folder to deposit results in

If the workflow requires input files or parameters, corresponding configuration sections will be shown.

../../_images/workflow_submit.pngTask submission

After all fields have been configured, click the Start button to submit the task.

Monitor status#

After a moment the task page will appear. At first there may be no log messages.

../../_images/task_created.pngTask status: CREATED

Before long the task should be created, scheduled, and started on the appropriate agent. At this point you should see a few lines of log output:

../../_images/task_running.pngTask status: RUNNING

When a task completes successfully, the status will change from RUNNING to COMPLETED.

../../_images/task_completed.pngTask status: COMPLETED

Retrieve results#

The output folder in the CyVerse data store section will eventually open at the bottom of the view (you may need to reload the page). Results will be zipped into a file with name matching the task’s ID.

../../_images/task_results.pngTask results