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Updated Docker description

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William L Hamilton 2017-10-12 15:13:49 -07:00 committed by GitHub
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README.md
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@ -32,7 +32,25 @@ If you make use of this code or the GraphSage algorithm in your work, please cit
### Requirements ### 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 ### 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. *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 #### Input format
As input, at minimum the code requires that a --train_prefix option is specified which specifies the following data files: As input, at minimum the code requires that a --train_prefix option is specified which specifies the following data files: