- 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**. 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
Save the generated environment in the directory to be mounted (e.g. we have conveniently created an empty directory in the `ml-agents` repository called`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`.
--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.
**Notes On Mount Argumentss**
- `source` : Reference to the path in your host OS where you will store the Unity executable.
- `target`: Tells docker to mount the `source` path as a disk with this name.
- `docker-target-name`: Tells the ML-Agents python package what the name of the disk where it can read the Unity executable and store the graph.*This should therefore be identical to the `target`.
- `train`: Argument passed to `learn.py` to run ML-Agents in trainer mode.
- `run-id`: Argument passed to `learn.py` for a unique identifier for an experiment.
For more details on docker mounts, look at [these](https://docs.docker.com/storage/bind-mounts/) docs from Docker.
eshvk