Managing External Services

How to manage external, longer-lived services

ZenML interacts with external systems (e.g. prediction services, monitoring systems, visualization services) via a so-called Service abstraction. The concrete implementation of this abstraction deals with functionality concerning the life-cycle management and tracking of an external service (e.g. process, container, Kubernetes deployment etc.).

Using Services in Steps

Services can be passed through steps like any other object, and used to interact with the external systems that they represent:

from zenml.steps import step

@step
def my_step(my_service: MyService) -> ...:
    if not my_service.is_running:
        my_service.start()  # starts service
    my_service.stop()  # stops service

Examples

One concrete example of a Service is the built-in LocalDaemonService, a service represented by a local daemon process which extends the base Service class with functionality concerning the life-cycle management and tracking of local daemon processes.

Another example is the TensorboardService which enables visualizing TensorBoard logs by managing a local TensorBoard server, which couples nicely with the TensorboardVisualizer to visualize Tensorboard logs:

from zenml.integrations.tensorboard.services.tensorboard_service import (
    TensorboardService,
    TensorboardServiceConfig
)

service = TensorboardService(
    TensorboardServiceConfig(
        logdir=logdir,
    )
)

# start the service
service.start(timeout=20)

# stop the service
service.stop()

You can find full examples of using services here:

• Visualizing training with TensorBoard in the Kubeflow TensorBoard example.
• Interacting with the services of deployed models in the MLflow deployment example.
• Interacting with the services of deployed models in the Seldon deployment example.
• Interacting with the services of deployed models in the KServe deployment example.