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.
We are currently offering an experimental solution for Windows and Mac users who would 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 TensorFlow and Unity to rely on _only_ the CPU for computation purposes. As such, we currently only support training using environments that 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))
## 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_).
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_).
- 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`.
- 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`.
--train --run-id=<run-id>
```
For our balance ball, example this would be:
For the `3DBall` environment, for example this would be:
Arthur Juliani
7 年前