> ## Documentation Index
> Fetch the complete documentation index at: https://zenml.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 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:

```py
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](https://www.tensorflow.org/tensorboard) logs by managing a local TensorBoard server, which couples nicely with the `TensorboardVisualizer` to visualize Tensorboard logs:

```py
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](https://github.com/zenml-io/zenml/tree/main/examples/kubeflow%5Fpipelines%5Forchestration).
* Interacting with the services of deployed models in the [MLflow deployment example](https://github.com/zenml-io/zenml/tree/main/examples/mlflow%5Fdeployment).
* Interacting with the services of deployed models in the [Seldon deployment example](https://github.com/zenml-io/zenml/tree/main/examples/seldon%5Fdeployment).
* Interacting with the services of deployed models in the [KServe deployment example](https://github.com/zenml-io/zenml/tree/main/examples/kserve%5Fdeployment).