adding Dockerfiles

.dockerignore

@@ -0,0 +1,3 @@
+.git
+Dockerfile*
+.gitignore

Dockerfile

@@ -0,0 +1,6 @@
+FROM gcr.io/tensorflow/tensorflow:1.3.0
+
+RUN pip install networkx==1.11
+RUN rm /notebooks/*
+
+COPY . /notebooks

Dockerfile.gpu

@@ -0,0 +1,6 @@
+FROM gcr.io/tensorflow/tensorflow:1.3.0-gpu
+
+RUN pip install networkx==1.11
+RUN rm /notebooks/*
+
+COPY . /notebooks

README.md

@@ -48,6 +48,22 @@ We generally found that performance continued to improve even after the loss was
 
 *Note:* For the PPI data, and any other multi-ouput dataset that allows individual nodes to belong to multiple classes, it is necessary to set the `--sigmoid` flag during supervised training. By default the model assumes that the dataset is in the "one-hot" categorical setting.
 
+#### Docker
+
+You can run GraphSage inside a [docker](https://docs.docker.com/) image. After cloning the project, build and run the image as following:
+
+	$ docker build -t graphsage .
+	$ docker run -it graphsage bash
+
+or start a Jupyter Notebook instead of bash:
+
+	$ docker run -it -p 8888:8888 graphsage
+
+You can also run the GPU image using [nvidia-docker](https://github.com/NVIDIA/nvidia-docker):
+
+	$ docker build -t graphsage:gpu -f Dockerfile.gpu .
+	$ nvidia-docker run -it graphsage:gpu bash	
+
 #### Input format
 As input, at minimum the code requires that a --train_prefix option is specified which specifies the following data files: