Local Docker Orchestrator

Orchestrating your pipelines to run in Docker.

The local Docker orchestrator is an orchestrator flavor that comes built-in with ZenML and runs your pipelines locally using Docker.

When to use it

You should use the local Docker orchestrator if:

• you want the steps of your pipeline to run locally in isolated environments.

• you want to debug issues that happen when running your pipeline in Docker containers without waiting and paying for remote infrastructure.

How to deploy it

To use the local Docker orchestrator, you only need to have Docker installed and running.

How to use it

To use the local Docker orchestrator, we can register it and use it in our active stack:

zenml orchestrator register <ORCHESTRATOR_NAME> --flavor=local_docker

# Register and activate a stack with the new orchestrator
zenml stack register <STACK_NAME> -o <ORCHESTRATOR_NAME> ... --set

You can now run any ZenML pipeline using the local Docker orchestrator:

python file_that_runs_a_zenml_pipeline.py

Additional configuration

For additional configuration of the Local Docker orchestrator, you can pass LocalDockerOrchestratorSettings 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. A full list of what can be passed in via the run_args can be found in the Docker Python SDK documentation.

For more information and a full list of configurable attributes of the local Docker orchestrator, check out the SDK Docs .

For example, if you wanted to specify the CPU count available for the Docker image (note: only configurable for Windows), you could write a simple pipeline like the following:

from zenml import step, pipeline
from zenml.orchestrators.local_docker.local_docker_orchestrator import (
    LocalDockerOrchestratorSettings,
)


@step
def return_one() -> int:
    return 1


settings = {
    "orchestrator": LocalDockerOrchestratorSettings(
        run_args={"cpu_count": 3}
    )
}


@pipeline(settings=settings)
def simple_pipeline():
    return_one()

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.