From 8062f032fd16acfb04f72c495d4829b9d07353b3 Mon Sep 17 00:00:00 2001 From: William L Hamilton Date: Thu, 12 Oct 2017 15:13:49 -0700 Subject: [PATCH] Updated Docker description --- README.md | 35 +++++++++++++++++++---------------- 1 file changed, 19 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index c1a522a..5f09e26 100644 --- a/README.md +++ b/README.md @@ -32,7 +32,25 @@ If you make use of this code or the GraphSage algorithm in your work, please cit ### Requirements -Recent versions of TensorFlow, numpy, scipy, and networkx are required. +Recent versions of TensorFlow, numpy, scipy, and networkx are required (but networkx must be <=1.11). To guarantee that you have the right package versions, you can use [docker](https://docs.docker.com/) to easily set up a virtual environment. See the Docker subsection below for more info. + +#### Docker + +If you do not have [docker](https://docs.docker.com/) installed, you will need to do so. (Just click on the preceding link, the installation is pretty painless). + +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 ### Running the code @@ -48,21 +66,6 @@ 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: