diff --git a/docs/Training-on-Amazon-Web-Service.md b/docs/Training-on-Amazon-Web-Service.md index 1f3b080f3d..a74d814bda 100644 --- a/docs/Training-on-Amazon-Web-Service.md +++ b/docs/Training-on-Amazon-Web-Service.md @@ -1,53 +1,68 @@ # Training on Amazon Web Service -This page contains instructions for setting up an EC2 instance on Amazon Web Service for use in training ML-Agents environments. Current limitations of the Unity Engine require that a screen be available to render to. In order to make this possible when training on a remote server, a virtual screen is required. We can do this by installing Xorg and creating a virtual screen. Once installed and created, we can display the Unity environment in the virtual environment, and train as we would on a local machine. +This page contains instructions for setting up an EC2 instance on Amazon Web Service for training ML-Agents environments. You can run "headless" training if none of the agents in the environment use visual observations. ## Pre-Configured AMI A public pre-configured AMI is available with the ID: `ami-30ec184a` in the `us-east-1` region. It was created as a modification of the Amazon Deep Learning [AMI](https://aws.amazon.com/marketplace/pp/B01M0AXXQB). ## Configuring your own Instance -Instructions here are adapted from this [Medium post](https://medium.com/towards-data-science/how-to-run-unity-on-amazon-cloud-or-without-monitor-3c10ce022639) on running general Unity applications in the cloud. 1. To begin with, you will need an EC2 instance which contains the latest Nvidia drivers, CUDA8, and cuDNN. There are a number of external tutorials which describe this, such as: * [Getting CUDA 8 to Work With openAI Gym on AWS and Compiling TensorFlow for CUDA 8 Compatibility](https://davidsanwald.github.io/2016/11/13/building-tensorflow-with-gpu-support.html) * [Installing TensorFlow on an AWS EC2 P2 GPU Instance](http://expressionflow.com/2016/10/09/installing-tensorflow-on-an-aws-ec2-p2-gpu-instance/) * [Updating Nvidia CUDA to 8.0.x in Ubuntu 16.04 – EC2 Gx instance](https://aichamp.wordpress.com/2016/11/09/updating-nvidia-cuda-to-8-0-x-in-ubuntu-16-04-ec2-gx-instance/) -2. Move `python` to remote instance. + +## Installing ML-Agents + +2. Move `python` sub-folder of this ml-agents repo to the remote ECS instance, and set it as the working directory. 2. Install the required packages with `pip3 install .`. -3. Run the following commands to install Xorg: + +## Testing + +To verify that all steps worked correctly: + +1. In the Unity Editor, load a project containing an ML-Agents environment (you can use one of the example environments if you have not created your own). +2. Open the Build Settings window (menu: File > Build Settings). +3. Select Linux as the Target Platform, and x64_86 as the target architecture. +4. Check Headless Mode (unless you have enabled a virtual screen following the instructions below). +5. Click Build to build the Unity environment executable. +6. Upload the executable to your EC2 instance. +7. Test the instance setup from Python using: + +```python +from unityagents import UnityEnvironment + +env = UnityEnvironment() +``` +Where `` corresponds to the path to your environment executable. + +You should receive a message confirming that the environment was loaded successfully. + +## (Optional) Enabling a virtual screen + +_Instructions here are adapted from this [Medium post](https://medium.com/towards-data-science/how-to-run-unity-on-amazon-cloud-or-without-monitor-3c10ce022639) on running general Unity applications in the cloud._ + +Current limitations of the Unity Engine require that a screen be available to render to when using visual observations. In order to make this possible when training on a remote server, a virtual screen is required. We can do this by installing Xorg and creating a virtual screen. Once installed and created, we can display the Unity environment in the virtual environment, and train as we would on a local machine. Ensure that `headless` mode is disabled when building linux executables which use visual observations. + +1. Run the following commands to install Xorg: + ``` sudo apt-get update sudo apt-get install -y xserver-xorg mesa-utils sudo nvidia-xconfig -a --use-display-device=None --virtual=1280x1024 ``` -4. Restart the EC2 instance. -## Launching your instance +2. Restart the EC2 instance. -1. Make sure there are no Xorg processes running. To kill the Xorg processes, run `sudo killall Xorg`. +3. Make sure there are no Xorg processes running. To kill the Xorg processes, run `sudo killall Xorg`. Note that you might have to run this command multiple times depending on how Xorg is configured. If you run `nvidia-smi`, you will have a list of processes running on the GPU, Xorg should not be in the list. -2. Run: +4. Run: + ``` sudo /usr/bin/X :0 & export DISPLAY=:0 ``` -3. To ensure the installation was successful, run `glxgears`. If there are no errors, then Xorg is correctly configured. -4. There is a bug in _Unity 2017.1_ which requires the uninstallation of `libxrandr2`, which can be removed with : -``` -sudo apt-get remove --purge libwxgtk3.0-0v5 -sudo apt-get remove --purge libxrandr2 -``` -This is scheduled to be fixed in 2017.3. - -## Testing - -If all steps worked correctly, upload an example binary built for Linux to the instance, and test it from Python with: -```python -from unityagents import UnityEnvironment - -env = UnityEnvironment(your_env) -``` - -You should receive a message confirming that the environment was loaded successfully. + +5. To ensure the installation was successful, run `glxgears`. If there are no errors, then Xorg is correctly configured. \ No newline at end of file