me will always be admin.

Additional configurationFor additional configuration of the Airflow orchestrator, you can pass AirflowOrchestratorSettings when defining or running your pipeline. Check out the SDK docs for a full list of available attributes and this docs page for more information on how to specify settings.

Enabling CUDA for GPU-backed hardware

Note that if you wish to use this orchestrator to run steps on a GPU, you will need to follow the instructions on this page to ensure that it works. It requires adding some extra settings customization and is essential to enable CUDA for the GPU to give its full acceleration.

Using different Airflow operators

Airflow operators specify how a step in your pipeline gets executed. As ZenML relies on Docker images to run pipeline steps, only operators that support executing a Docker image work in combination with ZenML. Airflow comes with two operators that support this:

the DockerOperator runs the Docker images for executing your pipeline steps on the same machine that your Airflow server is running on. For this to work, the server environment needs to have the apache-airflow-providers-docker package installed.

the KubernetesPodOperator runs the Docker image on a pod in the Kubernetes cluster that the Airflow server is deployed to. For this to work, the server environment needs to have the apache-airflow-providers-cncf-kubernetes package installed.

You can specify which operator to use and additional arguments to it as follows:

from zenml import pipeline, step

from zenml.integrations.airflow.flavors.airflow_orchestrator_flavor import AirflowOrchestratorSettings

airflow_settings = AirflowOrchestratorSettings(

operator="docker",  # or "kubernetes_pod"

# Dictionary of arguments to pass to the operator __init__ method

operator_args={}

# Using the operator for a single step

@step(settings={"orchestrator.airflow": airflow_settings})

def my_step(...):

# Using the operator for all steps in your pipeline