> ## 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.

# Local Docker Orchestrator

> How to orchestrate pipelines locally in Docker

The local Docker orchestrator is an [orchestrator](/component-gallery/orchestrators) flavor which 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](https://www.docker.com/) 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  --flavor=local_docker

# Register and activate a stack with the new orchestrator
zenml stack register  -o  ... --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 [API docs](https://apidocs.zenml.io/latest/core%5Fcode%5Fdocs/core-orchestrators/#zenml.orchestrators.local%5Fdocker.local%5Fdocker%5Forchestrator.LocalDockerOrchestratorSettings) for a full list of available attributes and [this docs page](/advanced-guide/pipelines/settings) for more information on how to specify settings.

For more information and a full list of configurable attributes of the local Docker orchestrator, check out the [API Docs](https://apidocs.zenml.io/latest/core%5Fcode%5Fdocs/core-orchestrators/#zenml.orchestrators.local%5Fdocker.local%5Fdocker%5Forchestrator.LocalDockerOrchestrator).

#### 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](/advanced-guide/pipelines/gpu-hardware) 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.