carla/Docs/build_docker.md

# Running CARLA in a Docker

This tutorial is designed for:

  * People that want to run CARLA without needing to install all dependencies
  * Recommended solution to run multiple CARLA servers and perform GPU mapping
  * People who don't need to render the full simulation (the server is headless)

This tutorial was tested in Ubuntu 16.04 and using NVIDIA 396.37 drivers.
This method requires a version of NVIDIA drivers >=390.


---
## Docker Installation

!!! note
    Docker requires sudo to run. Follow this guide to add users to the docker sudo
    group <https://docs.docker.com/install/linux/linux-postinstall/>

#### Docker CE

For our tests we used the Docker CE version.
To install Docker CE we recommend using [this tutorial][tutoriallink]

[tutoriallink]: https://docs.docker.com/install/linux/docker-ce/ubuntu/#extra-steps-for-aufs

#### NVIDIA-Docker2

To install nvidia-docker-2 we recommend using the "Quick Start"
section from the [nvidia-dockers github](https://github.com/NVIDIA/nvidia-docker).

---
## Getting it Running

Pull the CARLA image.

```sh
docker pull carlasim/carla:version
```

For selecting a version, for instance, version 0.8.2 (stable), do:

```sh
docker pull carlasim/carla:0.8.2
```

Running CARLA under docker:

```sh
docker run -p 2000-2002:2000-2002 --runtime=nvidia -e --gpus all carlasim/carla:0.8.4
```

The `-p 2000-2002:2000-2002` argument is to redirect host ports for the docker container.
Use `--gpus '"device=<gpu_01>,<gpu_02>"'` to specify which GPUs should run CARLA. Take a look at this [NVIDIA documentation](https://github.com/NVIDIA/nvidia-docker) to learn other syntax options.

You can also pass parameters to the CARLA executable. With this you can chose the town and
select the port that is going to be used:

```sh
docker run -p 2000-2002:2000-2002 --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0 carlasim/carla:0.8.4 /bin/bash CarlaUE4.sh  < Your list of parameters >
```

At the list of parameters do not forget to add `-world-port=<port_number>` so that CARLA runs on server mode
listening to the `<port_number>`