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@step

Marks a method in a FlowSpec as a Metaflow Step.

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Usage

from metaflow import FlowSpec, step

class MyFlow(FlowSpec):
    @step
    def start(self):
        print("Starting flow")
        self.next(self.end)
    
    @step
    def end(self):
        print("Flow complete")

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Description

The @step decorator identifies a method as a step in your Metaflow workflow. Each step represents a unit of work in your flow’s execution graph. Important: This decorator needs to be placed as close to the method as possible (i.e., before other decorators).

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Valid decorator ordering:

@batch
@step
def foo(self):
    pass

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Invalid decorator ordering:

@step
@batch
def foo(self):
    pass

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Parameters

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f
Callable[[FlowSpec], None] | Callable[[FlowSpec, Any], None]
required
Function to make into a Metaflow Step

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Returns

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Step Types

Metaflow supports several types of steps:

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Linear Step

A regular step that transitions to a single next step: 
@step
def process(self):
    self.result = self.data * 2
    self.next(self.end)

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Split Step

A step that branches into multiple parallel steps: 
@step
def start(self):
    self.next(self.process_a, self.process_b)

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Foreach Step

A step that creates multiple parallel tasks, one for each element: 
@step
def start(self):
    self.items = [1, 2, 3, 4, 5]
    self.next(self.process, foreach='items')

@step
def process(self):
    # Access the current item with self.input
    self.result = self.input * 2
    self.next(self.join)

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Join Step

A step that merges multiple parallel branches: 
@step
def join(self, inputs):
    # Merge artifacts from parallel branches
    self.merge_artifacts(inputs)
    self.next(self.end)

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Best Practices

  1. Keep steps focused: Each step should perform a single logical unit of work.
  2. Always call next(): Every step except the final end step must call self.next().
  3. Name steps clearly: Use descriptive names that indicate what the step does.
  4. Start and end: Flows typically start with a start step and end with an end step.

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