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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 11: Running rllab experiments on EC2\n",
+    "\n",
+    "This tutorial, a complement to tutorial 10, covers the process of running rllab experients on an Amazon EC2 instance. This tutorial assumes rllab has been installed correctly ([instructions](https://rllab.readthedocs.io/en/latest/user/installation.html)). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up rllab with AWS\n",
+    " \n",
+    "First, follow the [rllab cluster setup instructions](https://rllab.readthedocs.io/en/latest/user/cluster.html) with region `us-west-1`. Modify `rllab/config_personal.py` to reference the most current Flow Docker image (at the time of this writing, `evinitsky/flow`). [@fangyu is this true?]\n",
+    "\n",
+    "Navigate to your `flow` directory and modify `Makefile.template` per the instructions in that file.  The variable `RLLABDIR` should be the relative path from your `flow` directory to `rllab` and should not have a backslash at the end. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Running an experiment\n",
+    "\n",
+    "When running AWS experiments, your entire `rllab` directory is uploaded to AWS so that the files necessary for your experiment are available to the EC2 instance. Thus, commands are included to copy over your `flow` directory to your `rllab` root directory (this is the reason for the `RLLABDIR` variable above). \n",
+    "\n",
+    "- Before running an experiment, run `make prepare` from your `flow` directory.\n",
+    "- Ensure you have committed or otherwise tracked the state of your `flow` directory, because that instance is what will be used to run your experiment. Upon visualization, the same files will need to be used—for example, changes to environment's state-space would break the ability to run a trained policy using a different state space. Check out an old commit of your `flow` directory and run `make prepare` to use visualization tools included with rllab.\n",
+    "\n",
+    "`make clean` removes the debug directory and also all XML files in rllab root directory to reduce the size of the data to upload to AWS. If you are using already-existing network files (from, say, OpenStreetMap), ensure they do not get deleted by `make clean` by storing such files elsewhere.\n",
+    "\n",
+    "Inside the experiment, change the `mode` to `ec2` (other options are `local` and `local_docker`). You should run the experiment in `local_docker` mode briefly before running the `ec2` version to ensure there are no errors, particularly with Docker image compatability\n",
+    "\n",
+    "After running `python example.py` once the `mode` of `example.py` is `ec2`, you should see your experiment running on AWS."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Fetching Results\n",
+    "\n",
+    "- To get the results of your AWS experiments, navigate to your `rllab` directory and run `python scripts/sync_s3.py`. \n",
+    "- Your experiment results will be in `data/s3` in your `rllab` directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
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