Develop a custom data validator
How to develop a custom data validator
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How to develop a custom data validator
Last updated
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Base abstraction in progress!
We are actively working on the base abstraction for the Data Validators, which will be available soon. As a result, their extension is not recommended at the moment. When you are selecting a data validator for your stack, you can use one of .
If you need to implement your own Data Validator flavor, you can still do so, but keep in mind that you may have to refactor it when the base abstraction is updated.
ZenML comes equipped with that integrate a variety of data logging and validation libraries, frameworks and platforms. However, if you need to use a different library or service as a backend for your ZenML Data Validator, you can extend ZenML to provide your own custom Data Validator implementation.
If you want to implement your own custom Data Validator, you can follow the following steps:
Create a class which inherits from and override one or more of the abstract methods, depending on the capabilities of the underlying library/service that you want to integrate.
If you need any configuration, you can create a class which inherits from the BaseDataValidatorConfig class.
Bring both of these classes together by inheriting from the BaseDataValidatorFlavor.
(Optional) You should also provide some standard steps that others can easily insert into their pipelines for instant access to data validation features.
Once you are done with the implementation, you can register it through the CLI. Please ensure you point to the flavor class via dot notation:
For example, if your flavor class MyDataValidatorFlavor is defined in flavors/my_flavor.py, you'd register it by doing:
ZenML resolves the flavor class by taking the path where you initialized zenml (via zenml init) as the starting point of resolution. Therefore, please ensure you follow of initializing zenml at the root of your repository.
If ZenML does not find an initialized ZenML repository in any parent directory, it will default to the current working directory, but usually it's better to not have to rely on this mechanism, and initialize zenml at the root.
Afterwards, you should see the new flavor in the list of available flavors:
It is important to draw attention to when and how these base abstractions are coming into play in a ZenML workflow.
The CustomDataValidatorFlavor class is imported and utilized upon the creation of the custom flavor through the CLI.
The CustomDataValidatorConfig class is imported when someone tries to register/update a stack component with this custom flavor. Especially, during the registration process of the stack component, the config will be used to validate the values given by the user. As Config object are inherently pydantic objects, you can also add your own custom validators here.
The CustomDataValidator only comes into play when the component is ultimately in use.
The design behind this interaction lets us separate the configuration of the flavor from its implementation. This way we can register flavors and components even when the major dependencies behind their implementation are not installed in our local setting (assuming the CustomDataValidatorFlavor and the CustomDataValidatorConfig are implemented in a different module/path than the actual CustomDataValidator).
