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Workflow Spec

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Key Concepts

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defaults

At the top of the yaml workflow file you can specify default parameters to be used throughout the entire workflow. This includes environment variables, default machine instance configuration.

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inputs

The inputs block allows you to specify versioned dataset to be referenced and consumed by your jobs. Note, you can also collect these in a separate inputs.yaml file and reference this file when creating a workflow run.

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jobs

Jobs are also sometimes referred to as "steps" within the Gradient Workflow. A job is an individual task that executes code (such as a training a machine learning model) and can produce outputs (i.e. versioned datasets).

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Sample Workflow Spec

defaults:
  # default environment variables for all jobs, can use any supported
  # substitution syntax (named secrets, ephemeral secrets, etc...)
  env:
    HELLO: secret:hello
  # default instance type for all jobs
  resources:
    instance-type: metal-cpu

# workflow takes two inputs, neither of which have defaults. This means that
# when the workflow is run corresponding input corresponding for these values
# are required, example:
#
# {"inputs": {"echo": {"value": "hello world"}, "data": {"id": "test-one"}}}
#
inputs:
  data:
    type: dataset
  echo:
    type: string

jobs:
  job-1:
    # These are inputs for the "job-1" job, they are "aliases" to the
    # workflow inputs
    #
    # All inputs are placed in the "/inputs/<name>" path of the run
    # containers. So for this job we would have the paths "/inputs/data"
    # and "/inputs/echo".
    inputs:
      # The "/inputs/data" directory would contain the contents for the dataset
      # version.
      data: workflow.inputs.data
      # The "/inputs/echo" file would contain the string of the workflow input
      # "echo".
      echo: workflow.inputs.echo
     # These are outputs for the "job-1" job.
    #
    # All outputs are read from the "/outputs/<name>" path.
    outputs:
       # A directory will automatically be created for output datasets and
      # any content written to that directory will be committed to a newly
      # created dataset version when the jobs completes.
      data2:
        type: dataset
        with:
          id: test-two
       # The container is responsible creating th file "/outputs/<name>" with the
      # content being a small-ish utf-8 encoded string.
      echo2:
        type: string
     # Set job specific environment variables
    env:
      FOOBAR: test
    # Set action
    uses: container@v1
    # Set action arguments
    with:
      args:
      - bash
      - -c
      - find /inputs/data > /outputs/data2/list.txt; echo ENV $HELLO $FOOBAR > /outputs/echo2
      image: bash:5
  job-2:
    inputs:
      # these inputs use job-1 outputs instead of workflow inputs, you must
      # specify job-1 in the needs section to reference them here
      data2: job-1.outputs.data2
      echo2: job-1.outputs.echo2
    outputs:
      data3:
        type: dataset
        with:
          id: test-three
    # List of job-id's that must complete before this job runs
    needs:
    - job-1
    uses: container@v1
    with:
      args:
      - bash
      - -c
      - wc -l /inputs/data2/list.txt > /outputs/data3/summary.txt
      image: bash:5

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Example Workflow Run Output

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