Orchestrating the execution of ML pipelines.
The orchestrator is an essential component in any MLOps stack as it is responsible for running your machine learning pipelines. To do so, the orchestrator provides an environment that is set up to execute the steps of your pipeline. It also makes sure that the steps of your pipeline only get executed once all their inputs (which are outputs of previous steps of your pipeline) are available.
The orchestrator is a mandatory component in the ZenML stack. It is used to store all artifacts produced by pipeline runs, and you are required to configure it in all of your stacks.
Out of the box, ZenML comes with a
localorchestrator already part of the default stack that runs pipelines locally. Additional orchestrators are provided by integrations:
If you would like to see the available flavors of orchestrators, you can use the command:
zenml orchestrator flavor list
If your orchestrator comes with a separate user interface (for example Kubeflow, Airflow, Vertex), you can get the URL to the orchestrator UI of a specific pipeline run using the following code snippet:
from zenml.client import Client
pipeline_run = Client().get_pipeline_run("<PIPELINE_RUN_NAME>")
orchestrator_url = pipeline_run.metadata["orchestrator_url"].value