This describes in more detail the main components of a Gradient Workflow, as seen in the YAML file.
At the top of the YAML Workflow file, you can specify default parameters to be used throughout the entire Workflow. This includes environment variables and default machine instance configuration. Instances can also be specified per-job.
The inputs block allows you to specify named inputs (e.g., a versioned dataset) to be referenced and consumed by your jobs.
Note: you can also collect inputs in a separate YAML and reference this file as an inputPath when creating a Workflow run.
Workflow and job-level inputs can be of type: dataset (a persistent, versioned collection of data), string (e.g., a generated value or ID that may be output from another job) or volume (a temporary workspace mounted onto a job's container).
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 consume inputs and produce outputs.
Sample Workflow Spec
To run this Workflow, define datasets named test-one, test-two, and test-three as described in the Create Datasets for the Workflow documentation. Also, to make use of the secret named hello in the inputs section, define a secret as described here.
# Default environment variables for all jobs. Can use any supported