structure and quickstart

README.md

@@ -1,2 +1,43 @@
 # gpu-jupyter
-Leverage the power of Jupyter through your NVIDEA GPU and use Tensorflow and Pytorch in collaborative notebooks. 
+#### Leverage the power of Jupyter and use your NVIDEA GPU and use Tensorflow and Pytorch in collaborative notebooks. 
+
+
+## Contents
+
+1. [Requirements](#requirements)
+2. [Quickstart](#quickstart)
+3. [Deployment](#deployment-in-the-docker-swarm)
+3. [Configuration](#configuration)
+4. [Trouble-Shooting](#trouble-shooting)
+
+
+## Requirements
+
+1.  Install [Docker](https://www.docker.com/community-edition#/download) version **1.10.0+**
+2.  Install [Docker Compose](https://docs.docker.com/compose/install/) version **1.6.0+**
+
+3.  Get access to use your GPU via the CUDA drivers, see this [blog-post](https://medium.com/@christoph.schranz)
+4.  Clone this repository
+    ```bash
+    git clone https://github.com/iot-salzburg/gpu-jupyter.git
+    cd gpu-jupyter
+    ```
+
+## Quickstart
+
+As soon as you have access to your GPU locally (it can be tested via a Tensorflow or PyTorch), you can run these commands to start the jupyter notebook via docker-compose:
+  ```bash
+  ./start-local.sh
+  ```
+  
+This will run jupyter on the default port [localhost:8888](localhost:8888). The general usage is:
+  ```bash
+  ./start-local.sh -p [port]  # port must be an integer with 4 or more digits.
+  ```
+In order to stop the local deployment, run:
+
+  ```bash
+  ./stop-local.sh
+  ```
+ 
+