Automate from idea to production
Workflows are the newest (and most powerful) way to create machine learning projects. Workflows let you use a GitHub-action style syntax via YAML files to easily create powerful automation.
Workflows allow you to build complex, real-world machine learning projects. Note, this is an advanced topic so if you are still early in your ML journey, it might make more sense to start with Notebooks first.
Workflows are based on the Argo runtime engine which is a container-native continuous delivery tool for Kubernetes.
Gradient Workflows — Automate from idea to production

Key Terminology

  • Workflow: a named or unnamed entity that belongs to a team and project
    • Named workflows can be re-run with a default workflow spec, or be passed a new spec every time
  • Workflow Spec: a YAML list of jobs that is converted into an Argo template and run on the Gradient distributed runtime engine.
  • Job: self-contained part of a workflow spec that is similar to an Argo step
    • Jobs can define inputs, outputs, and their own environment variables
    • Jobs can require other jobs via "needs" and collect/pass info between jobs
    • Jobs can be implemented with an action via "use"
  • Action: a self-contained, composable set of code building blocks that can perform specific actions within a machine learning project.
    • Actions can receive parameters (e.g., args, image) within the job step via the "with" argument
    • E.g., [email protected] action = run a container, load inputs, and produce outputs
  • Workflow Run: the implementation of a workflow
    • The most basic run requires a workflowId and clusterId - most will also include a workflowSpec, and the inputs to be passed into the workflow
    • The workflow run contains everything needed for the workflow to actually be executed, i.e., what (workflowId), where (clusterId), how (workflowSpec), with (inputs, etc.)
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