rllab ec2 tutorial

docs/source/index.rst

@@ -19,7 +19,7 @@ Flow is a work in progress - input is welcome. Available documentation is limite
 
    intro
    flow_setup
-   Tutorials <https://github.com/flow-project/flow/tree/master/tutorials/>
+   tutorials
    multiagent
    rendering
    visualizing

docs/source/rllabec2.rst

@@ -0,0 +1,61 @@
+Running rllab experiments on EC2
+================================
+
+This page 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>`_).
+
+Setting up rllab with AWS
+-------------------------
+
+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.
+
+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**.
+
+Running an experiment
+---------------------
+
+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).
+
+-  Before running an experiment, run ``make prepare`` from your ``flow``
+   directory.
+-  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 your 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 before visualizing
+   your experiment results.
+
+``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.
+
+Inside the experiment, change the ``mode`` to ``ec2``. Other mode options are
+``local``, which uses your standard environment and ``local_docker``, which 
+uses a local Docker image to run the experiment. 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 compatibility.
+
+After running ``python example.py`` once the ``mode`` of ``example.py``
+is ``ec2``, you should see your experiment running on AWS.
+
+Fetching Results
+----------------
+
+-  To get the results of your AWS experiments, navigate to your
+   ``rllab`` directory and run ``python scripts/sync_s3.py``.
+-  Your experiment results will be in ``data/s3`` in your ``rllab``
+   directory.

docs/source/tutorials.rst

@@ -0,0 +1,12 @@
+Tutorials
+=========
+Tutorials for learning how to use Flow are available, both on GitHub
+and as part of this documentation.
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Links:
+
+   rllabec2
+   GitHub Tutorials <https://github.com/flow-project/flow/tree/master/tutorials/>

tutorials/tutorial11_rllab_ec2.ipynb

@@ -0,0 +1,81 @@
+{
+ "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."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  },
+  "widgets": {
+   "state": {},
+   "version": "1.1.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}