ml-agents/docs/Using-Docker.md

Using Docker For ML Agents (Experimental)

We are currently offering an experimental solution for Windows and Mac users who'd like to do training or inference using docker. This option may be appealing to users who would like to avoid dealing with Python and TensorFlow installation on their host machines. This setup currently forces both python and Unity to rely on only the CPU for computation purposes. As such, we currently only support training using environments which only contain agents which use vector observations, rather than camera-based visual observations. For example, the GridWorld environment which use visual observations for training is not supported.

Requirements

• Unity Linux Standalone Player (Link)
• Docker (Link)

Setup

• Install Docker (see link above) if you don't have it setup on your machine.

• Since Docker runs a container in an environment that is isolated from the host machine, we will be using a mounted directory, e.g. unity-volume in your host machine in order to share data, e.g. the Unity executable, curriculum files and tensorflow graph.

Usage

• Docker typically runs a container sharing a (linux) kernel with the host machine, this means that the Unity environment has to be built for the linux platform. From the Build Settings Window, please select the architecture to be x86_64 and choose the build to be headless (this is important because we are running it in a container that does not have graphics drivers installed). Save the generated environment in the directory to be mounted (e.g. we have conveniently created an empty directory called at the top level unity-volume).

• Ensure that
  unity-volume/<environment-name>.x86_64 and unity-volume/environment-name_Data. So for example, <environment_name> might be 3Dball and you might want to ensure that unity-volume/3Dball.x86_64 and unity-volume/3Dball_Data are both present in the directory unity-volume.

• Make sure the docker engine is running on your machine, then build the docker container by running docker build -t <image_name> . . in the top level of the source directory. Replace <image_name> by the name of the image that you want to use, e.g. balance.ball.v0.1.

• Run the container:

docker run --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
	 <tag-name>:latest <environment-name> \
	 --docker-target-name=unity-volume 
	 --train --run-id=<run-id>

For our balance ball, example this would be:

• Run the container:

docker run --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
	 balance.ball.v0.1:latest 3Dball \
	 --docker-target-name=unity-volume 
	 --train --run-id=<run-id>

Note The docker target volume name, unity-volume must be passed to ML-Agents as an argument using the --docker-target-name option. The output will be stored in mounted directory.