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
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
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
Setup Pykg2vec:
(pykg2vec) $ git clone https://github.com/Sujit-O/pykg2vec.git (pykg2vec) $ cd pykg2vec (pykg2vec) $ python setup.py install
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