Execution Overview
This section explains how to execute a workflow in the Hera runtime. First, we examine the usage pattern, then delve into the parameter structure.
Usage Pattern
To execute a Hera workflow, follow this pattern:
- Use
dh.new_workflow()
orproject.new_workflow()
to create the workflow, passing workflow parameters. - Call
workflow.run()
with the desired action, passing task parameters and run parameters.
# Create workflow with workflow parameters
workflow = dh.new_workflow(
name="my-workflow",
kind="hera",
code_src="pipeline.py",
handler="pipeline"
)
# Build the pipeline
run_build = workflow.run(
action="build" # Task parameter
)
# Execute the pipeline
run_pipeline = workflow.run(
action="pipeline", # Task parameter
parameters={"url": "https://example.com"} # Run parameter
)
Hera workflows are executed remotely on Kubernetes clusters managed by the platform.
Parameter Structure
Parameters are organized into three categories:
-
Workflow Parameters: Define the workflow's
spec
attributes, such as source code and execution environment. These are set when creating the workflow. -
Task Parameters: Specify the action type and execution environment configuration. For Hera runtimes, actions are
build
andpipeline
. -
Run Parameters: Control runtime behavior, such as pipeline parameters passed to the workflow function.
Task Actions
The Hera runtime supports two task actions:
build
: Build the pipeline definition in Argo YAMLpipeline
: Execute the built pipeline
Detailed Documentation
For comprehensive details on each parameter category:
- Workflow Parameters — Complete reference for workflow creation and configuration.
- Task Parameters — Execution modes and runtime settings.
- Run Parameters — Input/output mappings and execution options.