Start With pykg2vecΒΆ

In order to install pykg2vec, you will need setup the following libraries:

  • python >=3.7 (recommended)

  • pytorch>= 1.5

All dependent packages (requirements.txt) will be installed automatically when setting up pykg2vec.

  • networkx>=2.2

  • setuptools>=40.8.0

  • matplotlib>=3.0.3

  • numpy>=1.16.2

  • seaborn>=0.9.0

  • scikit_learn>=0.20.3

  • hyperopt>=0.1.2

  • progressbar2>=3.39.3

  • pathlib>=1.0.1

  • pandas>=0.24.2

Installation Guide

  1. Setup a Virtual Environment: we encourage you to use anaconda to work with pykg2vec:

    (base) $ conda create --name pykg2vec python=3.7
    (base) $ conda activate pykg2vec
  2. Setup Pytorch: we encourage to use pytorch with GPU support for good training performance. However, a CPU version also runs. The following sample commands are for setting up pytorch:

    # if you have a GPU with CUDA 10.1 installed
    (pykg2vec) $ conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
    # or cpu-only
    (pykg2vec) $ conda install pytorch torchvision cpuonly -c pytorch
  3. Setup Pykg2vec:

    (pykg2vec) $ git clone
    (pykg2vec) $ cd pykg2vec
    (pykg2vec) $ python install
  4. Validate the Installation: try the examples under /examples folder.

    # train TransE using benchmark dataset fb15k (use pykg2vec-train.exe on Windows)
    (pykg2vec) $ pykg2vec-train -mn transe -ds fb15k