Introducing a new library called Deep Graph Library (DGL) developed by the NYU & AWS teams, Shanghai. DGL is a package built on Python to simplify deep learning on graph, atop of existing deep learning frameworks.
DGL is essentially a Python package which serves as an interface between any existing tensor libraries and data that is expressed as graphs. It helps in easy implementation of graph neural networks such as Graph Convolution Networks, TreeLSTM and others. It also maintains high computation efficiency while doing this.
This new Python library is made in an effort to make graph implementations in deep learning simpler. According to the results they state, the improvement on some models is as high as 10 times and has better accuracy in some cases. Check out the results on GitHub.
Their website states: “We are keen to bring graphs closer to deep learning researchers. We want to make it easy to implement graph neural networks model family. We also want to make the combination of graph based modules and tensor based modules (PyTorch or MXNet) as smooth as possible.”
As of now, DGL supports PyTorch v1.0. The autobatching is up to 4x faster than DyNet.
DGL is tested on Ubuntu 16.04, macOS X, and Windows 10 and will work on any newer versions of these OSes. Python 3.5 or later is required while Python 3.4 or older is not tested. Support for Python 2 is in the works.
Installing it is as same as any other Python package.
pip install dgl
And with conda:
conda install -c dglteam dgl
Faster than Dynet is impressive, it has some quite clever autobatching !
— Guillaume Ausset (@aussetg) December 12, 2018
DGL is currently in the beta stage, licensed under Apache 2.0, and they have a Twitter page.
You can check out DGL at their website.