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Decorators are a core feature of Metaflow that allow you to modify the behavior of steps and flows. They are implemented as Python decorators (using the @ syntax) and can be applied to step methods or flow classes.

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Step Decorators

Step decorators modify the behavior of individual steps in your flow. They control where and how steps execute, handle failures, set timeouts, and more.

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Execution Environment

  • @step - Declares a method as a step in the flow
  • @batch - Execute step on AWS Batch
  • @kubernetes - Execute step on Kubernetes
  • @resources - Specify compute resources (CPU, memory, GPU)

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Error Handling

  • @retry - Automatically retry failed steps
  • @timeout - Set execution time limits
  • @catch - Gracefully handle step failures

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Environment Configuration

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Flow Decorators

Flow decorators modify the behavior of the entire flow and are applied to the FlowSpec class.
  • @project - Organize flows into projects with namespaces

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Usage

Decorators are applied using Python’s @ syntax: 
from metaflow import FlowSpec, step, batch, retry

class MyFlow(FlowSpec):
    @retry(times=3)
    @batch(cpu=4, memory=8192)
    @step
    def process_data(self):
        # Your code here
        pass

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Decorator Order

When using multiple decorators on a step, the order matters:
  1. Error handling decorators (@retry, @catch) should be at the top
  2. Environment decorators (@batch, @kubernetes, @resources) come next
  3. The @step decorator must always be the last decorator (closest to the method)
@retry(times=3)        # 1. Error handling
@timeout(hours=1)      # 2. Constraints
@batch(cpu=4)          # 3. Execution environment
@step                  # 4. Always last
def my_step(self):
    pass

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Using —with

Many decorators can be applied dynamically at runtime using the --with command-line option: 
python myflow.py run --with batch:cpu=8,memory=16384
This is useful for testing different resource configurations without modifying your code.

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