Step Parameterization and Caching

Iteration is native to ZenML.
Machine learning pipelines are rerun many times over throughout their development lifecycle.

Parameterizing steps

In order to iterate quickly, one must be able to quickly tweak pipeline runs by changing various parameters of steps within a pipeline.
You can configure your pipelines at runtime in the following ways:
  • BaseParameters: Runtime configuration passed down as a parameter to step functions.
  • BaseSettings: Runtime settings passed down to stack components and pipelines.
In this section, we will focus on BaseParameters, and in the Advanced Guide we will dive deeper into BaseSettings.
You can parameterize a step by creating a subclass of the BaseParameters. When such a config object is passed to a step, it is not treated like other artifacts. Instead, it gets passed into the step when the pipeline is instantiated.
import numpy as np
from sklearn.base import ClassifierMixin
from sklearn.svm import SVC
from zenml.steps import step, BaseParameters
class SVCTrainerParams(BaseParameters):
"""Trainer params"""
gamma: float = 0.001
def svc_trainer(
params: SVCTrainerParams,
X_train: np.ndarray,
y_train: np.ndarray,
) -> ClassifierMixin:
"""Train a sklearn SVC classifier."""
model = SVC(gamma=params.gamma)
model.fit(X_train, y_train)
return model
The default value for the gamma parameter is set to 0.001. However, when the pipeline is instantiated you can override the default like this:
first_pipeline_instance = first_pipeline(
Behind the scenes, BaseParameters is implemented as a Pydantic BaseModel. Therefore, any type that Pydantic supports is also supported as an attribute type in the BaseParameters.
Try running the above pipeline, and changing the parameter gamma through many runs. In essence, each pipeline can be viewed as an experiment, and each run is a trial of the experiment, defined by the BaseParameters. You can always get the parameters again when you fetch pipeline runs, to compare various runs.

Caching in ZenML

When you tweaked the gamma variable above, you must have noticed that the digits_data_loader step does not re-execute for each subsequent run. This is because ZenML understands that nothing has changed between subsequent runs, so it re-uses the output of the last run (the outputs are persisted in the artifact store. This behavior is known as caching.
Prototyping is often a fast and iterative process that benefits a lot from caching. This makes caching a very powerful tool. Checkout this ZenML Blogpost on Caching for more context on the benefits of caching and ZenBytes lesson 1.2 for a detailed example on how to configure and visualize caching.
ZenML comes with caching enabled by default. Since ZenML automatically tracks and versions all inputs, outputs, and parameters of steps and pipelines, ZenML will not re-execute steps within the same pipeline on subsequent pipeline runs as long as there is no change in these three.
Currently, the caching does not automatically detect changes within the file system or on external APIs. Make sure to set caching to False on steps that depend on external inputs or if the step should run regardless of caching.

Configuring caching behavior of your pipelines

Although caching is desirable in many circumstances, one might want to disable it in certain instances. For example, if you are quickly prototyping with changing step definitions or you have an external API state change in your function that ZenML does not detect.
There are multiple ways to take control of when and where caching is used:

Disabling caching for the entire pipeline

On a pipeline level the caching policy can be set as a parameter within the decorator.
def first_pipeline(....):
"""Pipeline with cache disabled"""
If caching is explicitly turned off on a pipeline level, all steps are run without caching, even if caching is set to True for single steps.

Disabling caching for individual steps

Caching can also be explicitly turned off at a step level. You might want to turn off caching for steps that take external input (like fetching data from an API or File IO).
def import_data_from_api(...):
"""Import most up-to-date data from public api"""
You can get a graphical visualization of which steps were cached using the ZenML Dashboard.

Dynamically disabling caching for a pipeline run

Sometimes you want to have control over caching at runtime instead of defaulting to the backed in configurations of your pipeline and its steps. ZenML offers a way to override all caching settings of the pipeline at runtime.
first_pipeline(step_1=..., step_2=...).run(enable_cache=False)

Code Example

The following example shows caching in action with the code example from the previous section.
For a more detailed example on how caching is used at ZenML and how it works under the hood, checkout ZenBytes lesson 1.2!
Code Example of this Section