Using Docker to run garage

Currently there are two types of garage images:

• headless: garage without environment visualization.
• nvidia: garage with environment visualization using an NVIDIA graphics card.

Headless image

If you already have a copy of garage, proceed to subsection ["Build and run the image"], otherwise, keep reading.

To run an example launcher in the container, execute:

docker run -it --rm rlworkgroup/garage-headless python examples/tf/trpo_cartpole.py

To run environments using MuJoCo, pass the contents of the MuJoCo key in a variable named MJKEY in the same docker-run command using cat. For example, if your key is at ~/.mujoco/mjkey.txt, execute:

docker run \
  -it \
  --rm \
  -e MJKEY="$(cat ~/.mujoco/mjkey.txt)" \
  rlworkgroup/garage-headless python examples/tf/trpo_swimmer.py

To save the experiment data generated in the container, you need to specify a path where the files will be saved inside your host computer with the argument -v in the docker-run command. For example, if the path you want to use is at /home/tmp/data, execute:

docker run \
  -it \
  --rm \
  -v /home/tmp/data:/root/code/garage/data \
  rlworkgroup/garage-headless python examples/tf/trpo_cartpole.py

This binds a volume between your host path and the path in garage at /root/code/garage/data.

Build and run the image

To build the headless image, first clone this repository, move to the root folder of your local repository and then execute:

make build-headless

To build and run the container, execute;

make run-headless RUN_CMD="python examples/tf/trpo_cartpole.py"

Where RUN_CMD specifies the executable to run in the container.

The previous command adds a volume from the data folder inside your cloned garage repository to the data folder in the garage container, so any experiment results ran in the container will be saved in the data folder inside your cloned repository. The data is saved in a folder with the name of the container that generated the data, which by default is the name of the image type the container is based on with the date and time the container was launched.

If you want to specify another name for the container, do so with the variable CONTAINER_NAME:

make run-headless RUN_CMD="..." CONTAINER_NAME="my_container"

If you need to use MuJoCo, you need to place your key at ~/.mujoco/mjkey.txt or specify the corresponding path through the MJKEY_PATH variable:

make run-headless RUN_CMD="..." MJKEY_PATH="/home/user/mjkey.txt"

If you require to pass addtional arguments to the the make commands, you can use the variable ADD_ARGS, for example:

make build-headless ADD_ARGS="--build-arg MY_VAR=123"
make run-headless ADD_ARGS="-e MY_VAR=123"

Prerequisites

Be aware of the following prerequisites to build the image.

• Install Docker CE. Tested on version 18.09.0.
• Install Docker Compose. Tested on version 1.23.2.

Tested on Ubuntu 16.04. It's recommended to use the versions indicated above for docker-ce and docker-compose.

nvidia image

The same commands for the headless image mentioned above apply for the nvidia image, except that the image name is defined by rlworkgroup/garage-nvidia. For example, to execute a launcher file:

docker run -it --rm rlworkgroup/garage-nvidia python examples/tf/trpo_cartpole.py

Build and run the image

The same rules for the headless image apply here, except that the target names are the following:

make build-nvidia
make run-nvidia

Prerequisites

Additional to the prerequisites for the headless image, make sure to have:

• Install the latest NVIDIA driver, tested on nvidia-390
• Install nvidia-docker2

Tested on Ubuntu 16.04.