Update README

README.md

@@ -22,13 +22,13 @@ Please create an [issue](https://github.com/typicalTYLER/SolarPanelDataWrangler/
 
 One of the requirements is rtree, which requires you to install libspatialindex, instructions [here](http://toblerity.org/rtree/install.html).
 
-To install the environment, use the `setup/environment_cpu.yml` file, e.g.
+To install the environment, choose either the `setup/environment_cpu.yml` and `setup/environment_gpu.yml`. If you have GPU that you intend to use, you'll need to setup the relevant packages / drivers (e.g. cuDNN / CUDA, nvidia diver, etc.) before installing the conda environment. Once you've chosen a YML file, you can create the environment via something like the following:
 
 ```
 conda create --name spdw --file setup/environment_cpu.yml
 ```
 
-*Note*: This was created using `conda=4.6.11` and `python=3.6.8`.
+*Note*: These environments are built on `conda=4.6.11`, `python=3.6.8`, and `tensorflow=1.12.0`.
 
 ### DeepSolar repository
 
@@ -40,13 +40,27 @@ Your Mapbox API key must be in your environment variables as MAPBOX_ACCESS_TOKEN
 
 ## Docker Setup
 
-Within the `setup` directory is a `build_docker_images.sh` file that can be used to automatically setup a docker container that works out of the box with only a single additional step (adding your Mapbox API key). Once docker is set up, you simply need to specify a username that will be used inside the docker container, e.g. 
+Within the `setup` directory is a `build_docker_images.sh` script that can be used to automatically setup a docker container to develop out of.
+
+### No GPU Build 
+
+To build a docker image that uses the no GPU conda environment, first install docker. Next, choose a username that you would like to use inside the container, and from **within the setup directory**, run:
+
+```
+bash build_docker_images.sh --docker_user [specify username here]
+```
+
+Once the image is built, you should be able to work inside a container created from the image just as if you were logged into a remote instance.
+
+### GPU Build 
+
+To build a docker image that uses  the GPU conda environment, first install nvidia-docker (version 2.0). Next, make sure that you have the appropriate GPU driver installed for your system and for the version of tensorflow that will be used (`1.12.0`) as well as teh versions of CUDA and cuDNN (CUDA 9.0 and cuDNN7. Once that is done, choose a username that you would like to use inside the container, and from **within the setup directory**, run:
 
 ```
-bash setup/build_docker_images.sh --docker_user [specify username here]
+bash build_docker_images.sh --docker_user [specify username here] --gpu_build
 ```
 
-After they're created, you should be able to work inside the container using the conda `spdw` environment that is set up, inside the the `repos` directory for the username you specified.
+Once the image is built, you should be able to work inside a container created from the image just as if you were logged into a remote instance.
 
 # Overview