PRODIGE: Probabilistic Differentiable Graph Embeddings
A supplementary code for Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs. https://arxiv.org/pdf/1910.03524.pdf
What does it do?
What do i need to run it?
- Get as many CPUs as you can
- We do not support GPU pathfinding (yet)
- Use any popular 64-bit Linux operating system
- Tested on Ubuntu16.04, should work fine on most linux x64 and even MacOS;
- On other operating systems we recommend using Docker, e.g. pytorch-docker
- Install the libraries required to compile C++ parts of PRODIGE
sudo apt-get install gcc g++ libstdc++6 wget curl unzip git
sudo apt-get install swig3.0(or just swig)
How do I run it?
- Clone or download this repo.
cdyourself to it's root directory.
- Grab or build a working python enviromnent. Anaconda works fine.
- Install packages from
- It is critical that you use torch >= 1.1, not 1.0 or earlier
- You will also need jupyter or some other way to work with .ipynb files
- Open jupyter notebook in
./notebooks/and you're done!