lementation_class(self) -> Type[BaseStepOperator]:"""Returns the implementation class for this flavor."""

This is a slimmed-down version of the base implementation which aims to highlight the abstraction layer. In order to see the full implementation and get the complete docstrings, please check the SDK docs .

Build your own custom step operator

If you want to create your own custom flavor for a step operator, you can follow the following steps:

Create a class that inherits from the BaseStepOperator class and implement the abstract launch method. This method has two main responsibilities:Preparing a suitable execution environment (e.g. a Docker image): The general environment is highly dependent on the concrete step operator implementation, but for ZenML to be able to run the step it requires you to install some pip dependencies. The list of requirements needed to successfully execute the step can be found via the Docker settings info.pipeline.docker_settings passed to the launch() method. Additionally, you'll have to make sure that all the source code of your ZenML step and pipeline are available within this execution environment.Running the entrypoint command: Actually running a single step of a pipeline requires knowledge of many ZenML internals and is implemented in the zenml.step_operators.step_operator_entrypoint_configuration module. As long as your environment was set up correctly (see the previous bullet point), you can run the step using the command provided via the entrypoint_command argument of the launch() method.

If your step operator allows the specification of per-step resources, make sure to handle the resources defined on the step (info.config.resource_settings) that was passed to the launch() method.

If you need to provide any configuration, create a class that inherits from the BaseStepOperatorConfig class adds your configuration parameters.