Workflows are the newest (and most powerful) way to create machine learning projects. Workflows let you use a GitHub-action style syntax 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.
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 JSON or 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
[email protected] action = run a container, load inputs, and produce outputs
Workflow Run: the implementation of a workflow
the most basic run requires a
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.)