Actions and Workflows

Overview

Teaching: 0 min
Exercises: 0 min

Questions

• How can I automate a scientific exploration using a Github Actions workflow?

Objectives

• Explain what Github Actions workflows are.

• Explain how a Github Action workflow differs from shell scripts.

The workflow we saw previously has two steps:

1. Download data.
2. Execute analysis.

It’s time to write our first Github Actions workflow. To do so, create a wf.yml file and put it a myproject/ folder. The content of this file are the following:

steps:
- uses: docker://byrnedo/alpine-curl:0.1.8
  args: [-LO, https://github.com/datasets/co2-fossil-global/raw/master/global.csv]

- uses: docker://python:3.8.1-alpine
  runs: [scripts/get_mean_by_group.py, data/global.csv, "5"]

A workflow is specified with a steps keyword that denotes a YAML list. Each item in that list is a step, which is a YAML dictionary with at least a uses key in it. For more information on the syntax, check the official Popper documentation.

Running popper commands

Before we go ahead and execute the above workflow, let’s look at the syntax for running popper (sub) commands. The general pattern is the following:

popper <subcommand> [options]

Where <subcommand> is one of the available subcommands such as run, ci, version, etc. To get general help:

popper --help

And help information for each subcommand can also be displayed, for example:

popper run --help

Running a workflow

Now that we know how to execute commands, let’s run the workflow we showed above:

cd myproject
popper run -f wf.yml

The output of the above will inform you of what

Key Points