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Install and Setup

Install from wheel.

pip install graph-learn

Build from source.

Let's take Ubuntu 16.04 based on g++ 5.4.0 as an example to illustrate the steps of compiling the source code.

Install dependent libraries

sudo apt-get install git-all autoconf automake libtool libssl-dev cmake python-numpy python-setuptools python-pip

Build

git clone https://github.com/alibaba/graph-learn.git
cd graph-learn
git submodule update --init

Next, the entire project and test cases can be compiled in two ways.

  1. use Makefile(recommended):
make test
  1. Use CMakeLists.txt:
mkdir cmake-build && cd cmake-build
cmake -DTESTING=ON .. && make

build python package:

make python

If you need to execute a specific python bin, such as specifying compilation with python 3.7, then execute:

make python PYTHON=python3.7

Install

sudo pip install dist/your_wheel_name.whl

(Optional) Install TensorFlow

The TensorFlow model example provided by GL is developed based on TensorFlow 1.13 and requires the installation of the corresponding version of the library.

sudo pip install tensorflow==1.13.0

(Optional) Install PyTorch，PyG

The PyTorch model example provided by GL is based on PyG development and requires the installation of the corresponding library.

sudo pip install pytorch
# Install PyG follow the doc: https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html

Run UT.

source env.sh
./test_cpp_ut.sh
./test_python_ut.sh

Docker images

Graphlearn Docker hub

We provide a graphlearn image with the corresponding version of graphlearn pre-installed. Depending on the needs of algorithm development, we provide pre-installed images of Tensorflow 1.13.0rc1 and Pytorch 1.8.1+PyG, respectively. You can quickly start GraphLearn in the Docker image.

  1. Tensorflow1.13.0rc1, CPU
docker pull graphlearn/graphlearn:1.0.0-tensorflow1.13.0rc1-cpu

# or, pull the latest graphlearn with 1.0.0-tensorflow1.13.0rc1-cpu
docker pull graphlearn/graphlearn:latest

# or, pull the given version graphlearn with 1.0.0-tensorflow1.13.0rc1-cpu
docker pull graphlearn/graphlearn:1.0.0
  1. PyTorch1.8.1, Cuda10.2, cdnn7, with PyG
docker pull graphlearn/graphlearn:1.0.0-torch1.8.1-cuda10.2-cudnn7