Update AWS page

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(<your_env>)
+```
+Where `<your_env>` 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.