Hooks are a mechanism to add extra behaviour to Kedro’s main execution in an easy and consistent manner. Some examples may include:
- Adding a transformer after the data catalog is loaded
- Adding data validation to the inputs, before a node runs, and to the outputs, after a node has run. This makes it possible to integrate with other tools like Great-Expectations
- Adding machine learning metrics tracking, e.g. using MLflow, throughout a pipeline run
A Hook is comprised of a Hook specification and Hook implementation. To add Hooks to your project you will need to:
- Provide a Hook implementation for an existing Hook specification defined by Kedro
- Register your Hook implementation in your
Kedro defines Hook specifications for particular execution points where users can inject additional behaviour. Currently, the following Hook specifications are provided in kedro.framework.hooks:
The naming convention for non-error Hooks is
<before/after>_<noun>_<past_participle>, in which:
<past_participle>refers to when the Hook executed, e.g.
before <something> was runor
after <something> was created.
<noun>refers to the relevant component in the Kedro execution timeline for which this Hook adds extra behaviour, e.g.
The naming convention for error hooks is
on_<noun>_error, in which:
<noun>refers to the relevant component in the Kedro execution timeline that throws the error.
kedro.framework.hooks lists the full specifications for which you can inject additional behaviours by providing an implementation.
You should provide an implementation for the specification that describes the point at which you want to inject additional behaviour.
A Hook implementation should have the same name as the specification. It provides a concrete implementation with a subset of the specification’s parameters. For example, the full signature of the after_data_catalog_created Hook specification is:
@hook_spec def after_catalog_created( self, catalog: DataCatalog, conf_catalog: Dict[str, Any], conf_creds: Dict[str, Any], save_version: str, load_versions: Dict[str, str], run_id: str, ) -> None: pass
However, if you just want to use this Hook to add transformer for a data catalog after it is created, your Hook implementation can be as simple as:
# <your_project>/src/<your_project>/hooks.py from kedro.extras.transformers.time_profiler import ProfileTimeTransformer from kedro.framework.hooks import hook_impl from kedro.io import DataCatalog class TransformerHooks: @hook_impl def after_catalog_created(self, catalog: DataCatalog) -> None: catalog.add_transformer(ProfileTimeTransformer())
- To declare a Hook implementation, use the
- You only need to make use of a subset of arguments defined in the corresponding specification
- Group related Hook implementations under a namespace, preferably a class
- You can register more than one implementations for the same specification. They will be called in FIFO (first-in, first-out) order.
Registering your Hook implementations with Kedro¶
Hook implementations should be registered with Kedro through the
# <your_project>/src/<your_project>/run.py from your_project.hooks import TransformerHooks class ProjectContext(KedroContext): project_name = "kedro-tutorial" project_version = "0.16.2" hooks = ( # register the collection of your Hook implementations here. # Note that we are using an instance here, not a class. It could also be a module. TransformerHooks(), ) # You can add more than one hook by simply listing them # in a tuple.`hooks = (Hook1(), Hook2())` def _get_pipelines(self) -> Dict[str, Pipeline]: return create_pipelines()
This ensures that the
after_data_catalog_created implementation above will be called automatically after every time a data catalog is created.