- Dockerfile pulls in the mlagents directory now.
- Installs mlagents package locally with `pip install .`.
- Clients should now place trainer configs in unity-volume.
We currently offer a solution for Windows and Mac users who would like to do training or inference using Docker. This option may be appealing to those who would like to avoid installing Python and TensorFlow themselves. The current setup forces both TensorFlow and Unity to _only_ rely on the CPU for computations. Consequently, our Docker simulation does not use a GPU and uses [`Xvfb`](https://en.wikipedia.org/wiki/Xvfb) to do visual rendering. `Xvfb` is a utility that enables `ML-Agents` (or any other application) to do rendering virtually i.e. it does not assume that the machine running `ML-Agents` has a GPU or a display attached to it. This means that rich environments which involve agents using camera-based visual observations might be slower.
We currently offer a solution for Windows and Mac users who would like to do
training or inference using Docker. This option may be appealing to those who
would like to avoid installing Python and TensorFlow themselves. The current
setup forces both TensorFlow and Unity to _only_ rely on the CPU for
computations. Consequently, our Docker simulation does not use a GPU and uses
[`Xvfb`](https://en.wikipedia.org/wiki/Xvfb) to do visual rendering. `Xvfb` is a
utility that enables `ML-Agents` (or any other application) to do rendering
virtually i.e. it does not assume that the machine running `ML-Agents` has a GPU
or a display attached to it. This means that rich environments which involve
agents using camera-based visual observations might be slower.
- [Download](https://unity3d.com/get-unity/download) the Unity Installer and
add the _Linux Build Support_ Component
- [Download](https://unity3d.com/get-unity/download) the Unity Installer and add
the _Linux Build Support_ Component
- [Download](https://www.docker.com/community-edition#/download) and
install Docker if you don't have it setup on your machine.
- [Download](https://www.docker.com/community-edition#/download) and install
Docker 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, a mounted directory in your host machine is used to share data, e.g. the Unity executable, curriculum files and TensorFlow graph. For convenience, we created an empty `unity-volume` directory at the root of the repository for this purpose, but feel free to use any other directory. The remainder of this guide assumes that the `unity-volume` directory is the one used.
- Since Docker runs a container in an environment that is isolated from the host
machine, a mounted directory in your host machine is used to share data, e.g.
the Unity executable, curriculum files and TensorFlow graph. For convenience,
we created an empty `unity-volume` directory at the root of the repository for
this purpose, but feel free to use any other directory. The remainder of this
guide assumes that the `unity-volume` directory is the one used.
Using Docker for ML-Agents involves three steps: building the Unity environment with specific flags, building a Docker container and, finally, running the container. If you are not familiar with building a Unity environment for ML-Agents, please read through our [Getting Started with the 3D Balance Ball Example](Getting-Started-with-Balance-Ball.md) guide first.
Using Docker for ML-Agents involves three steps: building the Unity environment
with specific flags, building a Docker container and, finally, running the
container. If you are not familiar with building a Unity environment for
ML-Agents, please read through our [Getting Started with the 3D Balance Ball
Since Docker typically runs a container sharing a (linux) kernel with the host machine, the
Unity environment **has** to be built for the **linux platform**. When building a Unity environment, please select the following options from the the Build Settings window:
Since Docker typically runs a container sharing a (linux) kernel with the host
machine, the Unity environment **has** to be built for the **linux platform**.
When building a Unity environment, please select the following options from the
the Build Settings window:
- If the environment does not contain visual observations, you can select the `headless` option here.
- If the environment does not contain visual observations, you can select the
`headless` option here.
Then click `Build`, pick an environment name (e.g. `3DBall`) and set the output directory to `unity-volume`. After building, ensure that the file `<environment-name>.x86_64` and subdirectory `<environment-name>_Data/` are created under `unity-volume`.
Then click `Build`, pick an environment name (e.g. `3DBall`) and set the output
directory to `unity-volume`. After building, ensure that the file
`<environment-name>.x86_64` and subdirectory `<environment-name>_Data/` are
created under `unity-volume`.
First, make sure the Docker engine is running on your machine. Then build the Docker container by calling the following command at the top-level of the repository:
First, make sure the Docker engine is running on your machine. Then build the
Docker container by calling the following command at the top-level of the
repository:
```shell
docker build -t <image-name> .
docker build -t <image-name> .
```
Replace `<image-name>` with a name for the Docker image, e.g. `balance.ball.v0.1`.
**Note** if you modify hyperparameters in `trainer_config.yaml` you will have to build a new Docker Container before running.
Replace `<image-name>` with a name for the Docker image, e.g.
`balance.ball.v0.1`.
Run the Docker container by calling the following command at the top-level of the repository:
Run the Docker container by calling the following command at the top-level of
the repository:
```
```shell
<trainer-config-path> \
- `<container-name>` is used to identify the container (in case you want to interrupt and terminate it). This is optional and Docker will generate a random name if this is not set. _Note that this must be unique for every run of a Docker image._
- `<container-name>` is used to identify the container (in case you want to
interrupt and terminate it). This is optional and Docker will generate a
random name if this is not set. _Note that this must be unique for every run
of a Docker image._
- `<environemnt-name>`__(Optional)__: If you are training with a linux executable, this is the name of the executable. If you are training in the Editor, do not pass a `<environemnt-name>` argument and press the :arrow_forward: button in Unity when the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen.
- `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 `target`.**
- `train`, `run-id`: ML-Agents arguments passed to `learn.py`. `train` trains the algorithm, `run-id` is used to tag each experiment with a unique identifier.
- `<environemnt-name>`__(Optional)__: If you are training with a linux
executable, this is the name of the executable. If you are training in the
Editor, do not pass a `<environemnt-name>` argument and press the
:arrow_forward: button in Unity when the message _"Start training by pressing
the Play button in the Unity Editor"_ is displayed on the screen.
- `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 `target`.**
- `trainer-config-path`, `train`, `run-id`: ML-Agents arguments passed to
`learn.py`. `trainer-config-path` is the filepath of the trainer config file,
`train` trains the algorithm, and `run-id` is used to tag each experiment with
a unique identifier. We recommend placing the trainer-config file inside
`unity-volume` so that the container has access to the file.
For more detail on Docker mounts, check out [these](https://docs.docker.com/storage/bind-mounts/) docs from Docker.
For more detail on Docker mounts, check out
[these](https://docs.docker.com/storage/bind-mounts/) docs from Docker.
If you are satisfied with the training progress, you can stop the Docker container while saving state by either using `Ctrl+C` or `⌘+C` (Mac) or by using the following command:
If you are satisfied with the training progress, you can stop the Docker
container while saving state by either using `Ctrl+C` or `⌘+C` (Mac) or by using
the following command:
```
```shell
`<container-name>` is the name of the container specified in the earlier `docker run` command. If you didn't specify one, you can find the randomly generated identifier by running `docker container ls`.
`<container-name>` is the name of the container specified in the earlier `docker
run` command. If you didn't specify one, you can find the randomly generated
Deric Pang
6 年前