Add pre-configured ami

awjuliani · web-flow · commit b8f5bd64e11f · 2017-09-24T16:52:04.000-07:00
diff --git a/docs/Training-on-Amazon-Web-Service.md b/docs/Training-on-Amazon-Web-Service.md
@@ -2,6 +2,10 @@
 
 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. 
 
+## Pre-Configured AMI
+A public pre-configured AMI is available with the ID: `ami-30ec184a`. 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:
