Control caching behavior

By default steps in ZenML pipelines are cached whenever code and parameters stay unchanged.

@step(enable_cache=True) # set cache behavior at step level

def load_data(parameter: int) -> dict:

...

@step(enable_cache=False) # settings at step level override pipeline level

def train_model(data: dict) -> None:

...

@pipeline(enable_cache=True) # set cache behavior at step level

def simple_ml_pipeline(parameter: int):

...

Caching only happens when code and parameters stay the same.

Like many other step and pipeline settings, you can also change this afterward:

# Same as passing it in the step decorator

my_step.configure(enable_cache=...)

# Same as passing it in the pipeline decorator

my_pipeline.configure(enable_cache=...)

Find out here how to configure this in a YAML file

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Last updated 15 days ago