Batch runs and workflows

A workflow is a definition of execution of functions: it defines the order of execution of multiple dependent steps in a directed acyclic graph (DAG). A workflow can reference the project’s params, secrets, artifacts, etc. It can also use a function execution output as a function execution input (which, of course, defines the order of execution).

MLRun supports running workflows on a three types of engines, see Types of workflows, KFP, and Python.

Workflows are saved/registered in the project using the set_workflow().
Workflows are executed using the run() method or using the CLI command mlrun project.

See the examples listed below and the Machine learning tutorials for more details.

In this section

• Types of workflows, KFP, and Python
• MLRun execution context
• Decorators and auto-logging
• Configuring runs and functions
• Scheduled jobs and workflows
• Running a task (job)
• Running a multi-stage workflow
• Running a conditional workflow
• Running a multiple function workflow with ExitHandler
• Running a workflow with multiple functions in parallel