We are currently offering an experimental solution for people who'd like to do training or inference using docker. This setup currently forces both python and Unity to rely on _only_ the CPU for computation purposes. So we don't support environments such as [GridWorld](Example-Environments.md#gridworld) which use visual observations for training.
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](Example-Environments.md#gridworld) environment which use visual observations for training is not supported.
## Requirements
- Unity Linux Standalone Player ([Link](https://unity3d.com/get-unity/download?ref=professional&_ga=2.161111422.259506921.1519336396-1357272041.1488299149))
- Install [docker](https://www.docker.com/community-edition#/download) if you don't have it setup on your machine.
- Install Docker (see link above) if you don't have it setup on your machine.
Unity environment **has** to be built for the **linux platform**. 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 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`.
```
```
docker run --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
<tag-name>:latest <environment-name> \
--docker-target-name=unity-volume
For our balance ball, example this would be:
- Run the container:
```
```
docker run --mount type=bind,source="$(pwd)"/unity-volume,target=/unity-volume \
balance.ball.v0.1:latest 3Dball \
--docker-target-name=unity-volume
**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.
Arthur Juliani
6 年前