diff --git a/Dockerfile b/Dockerfile
index f308336..d489f50 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -2,7 +2,7 @@ from ubuntu
 RUN ["apt-get","update"]
 RUN ["apt-get","upgrade","-y"]
 RUN ["apt-get","install","-y","git", "python3-dev","tmux","locales","python3-pip","python3-numpy","python3-pandas","locales"]
-RUN ["pip3","install","pandas-gbq","tensorflow"]
+RUN ["pip3","install","pandas-gbq","tensorflow","git+https://hiplab.mc.vanderbilt.edu/git/aou/"]
 RUN ["mkdir","-p","/usr/apps"]
 WORKDIR /usr/apps
-RUN ["git","clone","https://hiplab.mc.vanderbilt.edu/git/aou/gan.git@release","aou-gan"]
+RUN ["git","clone","https://hiplab.mc.vanderbilt.edu/git/aou/bridge.git@release","aou-gan"]
diff --git a/README.md b/README.md
index 8eb92d1..48b01aa 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,49 @@
-# bridge
+## Introduction
+---
+
+This package is designed to generate synthetic data from a dataset from an original dataset using deep learning techniques
+
+    - Generative Adversarial Networks
+    - With "Earth mover's distance"
+
+## Installation
+---
+
+    pip install git+https://hiplab.mc.vanderbilt.edu/git/aou/data-maker.git@release
+
+## Usage
+---
+
+After installing the easiest way to get started is as follows (using pandas). The process is as follows:
+1. Train the GAN on the original/raw dataset
+
+
+        import pandas as pd
+        import data.maker
+        
+        df  = pd.read_csv('myfile.csv')
+        cols= ['f1','f2','f2']  
+        data.maker.train(data=df,cols=cols,logs='logs')
+
+2. Generate a candidate dataset from the learnt features
+
+
+    import pandas as pd
+    import data.maker
+
+    df = data.maker.generate(logs='logs')
+    df.head()
+    
+
+## Limitations
+---
+
+GANS will generate data assuming the original data has all the value space needed:
+    
+- No new data will be created
+    
+        Assuming we have a dataset with an gender attribute with values [M,F]. The synthetic data will not be able to generate genders outside [M,F]
+- Not advised on continuous values
+
+        GANS work well on discrete values and thus are not advised to be used to synthesize things like measurements (height, blood pressure, ...)