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Python annotation

As explained in Designing Deep Learning Systems: A guide for software engineers

An optional Python syntax feature that attaches metadata to function parameters, return values, variables, or other program elements.

Python annotations embed workflow structure directly in a Python DAG class. In Metaflow, annotations such as @step let the library package workflow code automatically, keeping prototyping and production execution aligned. section 9.3.3

Annotations define both workflow components and their relationships:

  • @step marks workflow functions as executable steps. section 9.3.3
  • self.next connects steps, including fan-out into parallel branches and later joins. section 9.3.3

Run an annotated workflow

A Metaflow workflow can display its DAG with `python data_process_workflow.py show`, run locally with `python data_process_workflow.py run`, or be created for AWS Step Functions or Argo Workflows.

How Designing Deep Learning Systems: A guide for software engineers covers it

How deep it goes
Defines step markers, transitions, inputs, and branching, but defers workflow packaging and execution to Metaflow.
Examples
Uses a data-processing workflow with parameters, transformations, database storage, notifications, fan-out, and local or production commands.
What it assumes
Assumes Python workflow code and introduces orchestration through annotated classes, functions, parameters, and transitions.
What you can do after
Apply it via Metaflow to construct, test, run, and deploy workflows without rewriting prototype code.
How it connects
Links Python code with workflow orchestration, directed acyclic graphs, production schedulers, and workflow versioning.
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