A Workflow is a unit of work wrapped into a container for execution in a SLURM cluster environment.

Using workflows#

For a tutorial on exploring and submitting plantit workflows, see the quickstart.

Binding workflows: the plantit.yaml file#

To host a Workflow on plantit, add a plantit.yaml file to some branch in any public GitHub repository.

Required attributes#

At minimum, the plantit.yaml file should look something like this:

name: Hello World
author: Groot            
image: docker://ubuntu
commands: echo "I am Groot!"

There are five required attributes:

  • name: the name of the workflow

  • author: the workflow’s author(s)

  • image: the Docker image to use

  • commands: the workflow’s entry point


The name the workflow will appear under in the plantit web UI. Need not be unique, however it’s generally best to try to give each workflow a unique, distinctive, relevant name. (This will help potential users of your software to find it via keyword search.)


The workflow’s author(s) or developer(s). Must be a string, although future versions of plantit may support a list of strings.


The Docker image to run the workflow with. Must be publicly available on Docker Hub. It’s a good idea to read the Singularity documentation on Docker/OCI interop before binding a workflow to plantit. Most phenotyping software is unlikely to encounter issues, but there are a few Singularity runtime compatibility caveats to be aware of.


Your workflow’s entry point. If you have multiple things to do, it’s generally best to put them in a script, rather than using && to append commands. (This may work for some image definitions, but is not guaranteed to.)

Optional attributes & sections#

There are a number of optional properties and sections as well:

  • public: whether the workflow should be accessible to all plantit users (defaults to false)

  • email: your (the workflow developer’s) preferred email address, e.g. for support/questions

  • shell: the shell to use to invoke your entry point (sh, bash, or zsh, defaults to bash)

  • gpu: whether this workflow should use GPUs (if available)

  • env: environment variables to provide to your container runtime(s)

  • params: parameters to be configured at submission time in the plantit web UI

  • input: the kind, names, patterns, and optionally, the default path of any inputs to the workflow

  • output: the kind, names and patterns of results expected from the workflow

  • jobqueue: the resources to request from the cluster scheduler


By default workflows are only visible to you in the plantit web UI. To make a workflow publicly accessible to all users, set the public attribute to true.


You can provide a contact email via an email attribute. If this attribute is provided, a mailto link will be shown in the user interface to allow your workflow’s users to easily contact you.


By default, the command specified in plantit.yaml is invoked directly from the Singularity container runtime, i.e., singularity exec <image> <command>. Since Singularity runs in a modified shell environment some behavior may differ from that produced by Docker. Some Anaconda-based images can be configured to automatically activate an environment, for instance. With Singularity this cannot be achieved without wrapping the command with bash -c '<command>' and editing bash startup files in the container definition.

For these reasons plantit provides a shell option. If provided, this option will cause plantit to wrap the command with ... <shell> -c 'command' when invoking Singularity. Supported values include:

  • bash

  • sh

  • zsh


To indicate that your workflow can take advantage of GPUs (only available on select deployment targets) add a gpu: True line to your configuration file. When deployed to environments with GPUs, your task will have access to an environment variable $GPUS, set to the number of GPU devices provided by the host.

Environment variables#

Certain environment variables will be automatically set in the Singularity container runtime when a workflow is submitted as a task, in case you need to reference them in your entry point command or script:

  • $WORKDIR: the current working directory

  • $INPUT: the path to an input file or directory

  • $OUTPUT: the path to the directory results will be written to

  • $INDEX: the index of the current input file (if there are multiple, otherwise defaults to 1 for single-file or -directory tasks)

You can provide custom environment variables in an env section, for instance:

  - LC_ALL=C.UTF-8
  - LANG=C.UTF-8


To parametrize your workflow, add a params section. For example, to allow the user to configure the message printed by the trivial workflow above:

name: Hello Who?
author: Groot
public: True
clone: False
image: docker://alpine
commands: echo "$MESSAGE"
 - name: message
   type: string

This will cause the value of message, specified in the plantit web UI at task submission time, to be substituted for $MESSAGE in the command at runtime.

Four parameter types are supported by plantit:

  • string

  • select

  • number

  • boolean

See the Computational-Plant-Science/plantit-example-parameters workflow on GitHub for an example of how to use parameters.

Default values#

To provide default values for your workflow’s parameters, you can use a default attribute. For instance:

 - name: message
   type: string
   default: 'Hello, world!'


plantit can automatically copy input files from the CyVerse Data Store or Data Commons onto the file system in your deployment environment, then push results back to the Data Store after your task completes. To configure inputs and outputs for a workflow, add input and output attributes to your configuration.


If your workflow requires inputs, add an input section to your configuration file, containing at minimum a path attribute (pointing either to a directory- or file-path in the CyVerse Data Store or Data Commons, or left blank) and a kind attribute indicating whether this workflow operates on a single file, multiple files, or an entire directory.

Input types (file, files, and directory)#

To indicate that your workflow should pull a single file from the Data Store and spawn a single container to process it, use kind: file. To pull a directory from the Data Store and spawn a container for each file, use kind: files. To pull a directory and spawn a single container to process it, use kind: directory.

It’s generally a good idea for path to reference a community-released or curated public dataset in the CyVerse Data Commons, so prospective users can test your workflow on real data. For instance, the plantit.yaml for a workflow which operates on a single file might have the following input section

  path: /iplant/home/shared/iplantcollaborative/testing_tools/cowsay/cowsay.txt
  kind: file
Input filetypes#

To specify which filetypes your workflow is permitted to accept, add a filetypes attribute to the input section:

  path: /iplant/home/shared/iplantcollaborative/testing_tools/cowsay
  kind: file
    - txt

Any values provided to filetypes will be joined (with ,) and substituted for FILETYPES in your workflow’s command. Use this to inform your code which filetypes to expect, for example:

commands: ls "$INPUT"/*.{"FILETYPES"} >> things_cow_say.txt
  path: /iplant/home/shared/iplantcollaborative/testing_tools/cowsay
  kind: file
    - txt
    - md     

Note that while the input.path and input.filetypes attributes are optional, you must provide a kind attribute if you provide an input section.


If your workflow produces outputs, add an output section with a path attribute to your configuration file. This attribute may be left blank if your workflow writes output files to the working directory; otherwise the value should be a directory path relative to the working directory. For example, to indicate that your workflow will deposit output files in a directory output/directory relative to the workflow’s working directory:

  path: output/directory

By default, all files under the given path are uploaded to the location in the CyVerse Data Store provided by the user. To explicitly indicate which files to include/exclude (this is suggested especially if you workflow deposits files in the working directory), add include and exclude sections under output:

  path: output/directory
    patterns:                           # include excel files
      - xlsx                            # and png files
      - important.jpg                   # but only this .jpg file
      - temp                            # don't include anything marked temp
      - not_important.xlsx              # and exclude a particularexcel file

If only an include section is provided, only the file patterns and names specified will be included. If only an exclude section is present, all files except the patterns and names specified will be included. If you provide both include and exclude sections, the include rules will first be applied to generate a subset of files, which will then be filtered by the exclude rules.

Jobqueue configuration#

To ensure your workflow takes optimal advantage of cluster resources, add a jobqueue section. To indicate that instances of your workflow should request 1 process and 1 core on 1 node with 1 GB of memory with 1 hour of walltime:

  walltime: "01:00:00"
  memory: "1GB"
  processes: 1
  cores: 1

If a jobqueue section is provided, all four attributes are required. If you do not provide a jobqueue section`, tasks will request 1 hour of walltime, 10 GB of RAM, 1 process, and 1 core on all agents.


When a plantit task is submitted, values provided for the jobqueue.walltime attribute will be passed through transparently to the selected deployment target’s scheduler. The plantit web UI will timestamp each task submission, If such a time limit is provided at submission time, plantit will attempt to cancel your task if it fails to complete before the time limit has elapsed.

Virtual memory#

Note that some deployment targets (namely the default public agent, TACC’s Stampede2) are equipped with virtual memory. For tasks deployed to agents with virtual memory, plantit will ignore values provided for the jobqueue.memory attribute and defer to the cluster scheduler: on Stampede2, for instance, all tasks have access to 98GB of RAM.

A simple example#

The following workflow prints the content of an input file and then writes it to an output file located in the same working directory.

name: Hello File
image: docker://alpine
public: True
commands: cat "$INPUT" && cat "$INPUT" >> cowsaid.txt
  from: /iplant/home/shared/iplantcollaborative/testing_tools/cowsay/cowsay.txt
  kind: file
  path: # blank to indicate working directory
      - cowsaid.txt

Repository refresh rate#

The plantit web application scrapes GitHub for repository information for all logged-in users every 5 minutes. (If you’ve just updated a repository, you may need to wait several minutes then reload the workflow page.)