Build garage Docker image from source

Garage source comes with a Makefile that contains recipes for building and running different Docker configurations for garage

Garage uses multi-stage Docker builds with the Docker BuildKit backend. The BuildKit backend is opt-in and needs to be enabled by setting environment variable DOCKER_BUILDKIT=1 in your shell. The Makefile takes care of that for you.

The important Docker related make targets are:

  • run-dev: builds and runs the Docker image with your copy of garage source installed. This builds the garage-dev target in Dockerfile and the resulting image is tagged as rlworkgroup/garage-dev

  • run-dev-nvidia: same as run-dev with CUDA 11.0 and cuDNN 8.0 for taking advantage of NVIDIA GPUs and also supports environment visualization. The build target is garage-dev-nvidia and the resulting image is tagged as rlworkgroup/garage-dev-nvidia

  • run-dev-nvidia-headless: same as run-dev-nvidia but without support for environment visualization. Suitable for running in headless mode for machines without a display.


Be aware of the following prerequisites to build the image.

  • Install Docker CE version 19.03 or higher. Tested on version 19.03.

Tested on Ubuntu 16.04, 18.04 and 20.04.

Build and run the garage-dev image

To build and run the headless image, first clone the garage repository, move to the root folder of your local repository and then execute;

make run-dev RUN_CMD="python examples/tf/"

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 Makefile uses the same username and uid as your current local account to create the default user in the Docker images. This keeps things simple by allowing the Docker user to write to the data directory without giving explicit permission.

By default, Docker generates random names for containers. If you want to specify a name for the container, you can do so with the variable CONTAINER_NAME. As a side effect, this will output the results in data/$CONTAINER_NAME directory instead of the data directory.

make run-dev RUN_CMD="..." CONTAINER_NAME="my_container_123"

This will output results in data/my_container_123 directory.

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-dev RUN_CMD="..." MJKEY_PATH="/home/user/mjkey.txt"

If you require to pass additional arguments to docker build and run commands, you can use the variables BUILD_ARGS and RUN_ARGS, for example:

make run-dev BUILD_ARGS="--build-arg MY_VAR=123" RUN_ARGS="-e MY_VAR=123"

Prerequisites for NVIDIA image

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

Tested on Ubuntu 18.04 & 20.04.

Build and run the NVIDIA image

The same rules for the headless image apply here, except that the target name is:

make run-dev-nvidia ...

This make command builds the NVIDIA image and runs it in a non-headless mode. It will not work on headless machines. You can run the NVIDIA in a headless state using the following target:

make run-dev-nvidia-headless ...

Expose GPUs for use

By default, garage-nvidia uses all of your gpus. If you want to customize which GPUs are used and/or want to set the GPU capabilities exposed, as described in official Docker documentation here, you can pass the desired values to --gpus option using the variable GPUS. For example:

make run-dev-nvidia GPUS="device=0,2" ...

Using a different CUDA version

The garage-nvidia Docker image uses nvidia/cuda:11.0-cudnn8-runtime-ubuntu18.04 as the parent image which requires NVIDIA driver version 450.36.06+. If you need to use garage with a different CUDA version, you might be able to build the garage-nvidia image from scratch using a different parent image using the variable PARENT_IMAGE.

make run-dev-nvidia PARENT_IMAGE="nvidia/cuda:11.0-cudnn8-runtime-ubuntu18.04" ...

You can find the required parent images at NVIDIA CUDA’s DockerHub

This page was authored by Gitanshu Sardana (@gitanshu), with contributions from Angel Ivan Gonzalez (@gonzaiva)