Adds instructions for using with AWS Deep Learning AMI and Docker

Author: philgooch

README.md

@@ -3,6 +3,21 @@
 ## Sequence-to-Sequence Tutorial with Github Issues Data
 Code For Medium Article: ["How To Create Data Products That Are Magical Using Sequence-to-Sequence Models"](https://medium.com/@hamelhusain/how-to-create-data-products-that-are-magical-using-sequence-to-sequence-models-703f86a231f8)
 
+## Installation
+
+`pip install -r requirements.txt`
+
+If you are using the AWS Deep Learning Ubuntu AMI, many of the required dependencies will already be installed,
+so you only need to run:
+
+```
+source activate tensorflow_p36
+pip install ktext annoy nltk pydot
+```
+
+See #4 below if you wish to run this tutorial using Docker.
+
+
 ## Resources:
 
 1. [Tutorial Notebook](https://nbviewer.jupyter.org/github/hamelsmu/Seq2Seq_Tutorial/blob/master/notebooks/Tutorial.ipynb):  The Jupyter notebook that coincides with the Medium post.
@@ -11,5 +26,18 @@ Code For Medium Article: ["How To Create Data Products That Are Magical Using Se
 
 3. [ktext](https://github.com/hamelsmu/ktext): this library is used in the tutorial to clean data.  This library can be installed with `pip`.  
 
-4. [Nvidia Docker Container](https://hub.docker.com/r/hamelsmu/seq2seq_tutorial/): contains all libraries that are required to run the tutorial.  This container is built with Nvidia-Docker v1.0.  You can run this container by executing `nvidia-docker run hamelsmu/seq2seq_tutorial/` after installing **Nvidia-Docker v1.0.** Note: I have not tested this on Nvidia-Docker v2.0.
+4. [Nvidia Docker Container](https://hub.docker.com/r/hamelsmu/seq2seq_tutorial/): contains all libraries that are required to run the tutorial.  This container is built with Nvidia-Docker v1.0.  You can install Nvidia-Docker and run this container like so:
+
+
+```
+curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey |   sudo apt-key add -
+distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
+curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list |   sudo tee /etc/apt/sources.list.d/nvidia-docker.list
+sudo apt-get update
+sudo apt-get install nvidia-docker
+
+sudo nvidia-docker run hamelsmu/seq2seq_tutorial
+
+```
 
+This should work with both Nvidia-Docker v1.0 and v2.0.