Gradient Actions

Gradient Actions are composable building blocks for creating reproducible machine learning workflows. Actions use the uses and with syntax to specify how a job step executes.

container

uses: container@v1
with:
  image: bash:3
  args: ["echo", "hello", "world"]

In this basic example, the Gradient action called container@v1 allows us to pick an arbitrary docker container (in this case the lightweight bash container) and pass arguments directly to it.

git-checkout

inputs:
  repo:
    type: volume
uses: git-checkout@v1
with:
  url: https://github.com/user/public-repo
  ref: 46aa59d6ecc3720ffe2454a6d9d360e6ce75acce #optional git ref

In this example, the Gradient action git-checkout@v1 clones the GitHub URL https://github.com/user/public-repo at ref 46aa59d6ecc3720ffe2454a6d9d360e6ce75acce into a volume named repo. The cloned files are accessible at inputs/<input-name> (in this case, inputs/repo), and subsequent jobs that specify a volume input can also access the repository files at inputs/<input-name>.