AI Shanghai Lablet
Amazon Web Services
Who Are We?
Amazon Web Services AI Shanghai Lablet was established in the fall of 2018 and is part of Amazon Web Services Machine Learning.
We focus on:
- Open-source projects such as the popular Deep Graph Library (DGL) framework,
- Fundamental research in the area of graph neural networks (GNNs) and their applications,
- Impact to Amazon Web Services customers through Amazon Web Services releases such as SageMaker DGL and Netpune ML, and
- Actively foster collaboration with the research community.
The Latest News
In the latest DGL v0.9.1, we released a new pipeline for preprocess, partition and dispatch graph of billions of nodes or edges for distributed GNN training. At its core is a new data format called Chunked Graph Data Format (CGDF) which stores graph data by chunks. The new pipeline processes data chunks in parallel which not only reduces the memory requirement of each machine but also significantly accelerates the entire procedure.
In the latest issue of Nature Machine Intelligence, Yousun Jung and his research team at Korea Institute of Science and Technology (KAIST) have proposed a novel generalized-template-based graph neural network called LocalTransform for accurate organic reactivity prediction . The generalized reaction templates demonstrate the built-in interpretability in addition to state-of-the-art prediction accuracy. LocalTransform has been implemented with DGL, DGL-LifeSci and PyTorch.
Six years after the first Graph Convolutional Networks paper, researchers are actively investigating more advanced GNN architecture or training methodology. As the developer team of DGL, we closely watch those new research trends and release features to facilitate them. Here, we're excited to announce a new major release DGL v0.9.
The new 0.8.2 update brings new additions for the GNN community.
V0.8.1 is a minor release that includes module updates, optimizations, new features and bug fixes.
We are excited to announce the release of DGL v0.8, which brings many new features as well as improvement on system performance.
There is an accelerated pace of industry adoption of GNNs. At the just-concluded GTC November 2021, CEO Jensen Huang highlighted the importance of GNN to the industry and announced the comprehensive partnership between NVIDIA and the DGL community. “Graphs — a key data structure in modern data science — can now be projected into deep-neural network frameworks with Deep Graph Library, or DGL, a new Python package.”, said Jensen.
On September 17, 2021, at " the 2021 Open Source Industry Conference" hosted by China Academy of Information and Communications Technology (CAICT), DGL [Deep Graph Library] from Amazon Web Services AI Shanghai Lab won " The 2021 OSCAR Peak Open Source Case Award"-Open Source Community and Open Source Project, highlighting technical strength and open source contributions of Amazon Web Services.
Our new paper "Bag of Tricks for Node Classification with Graph Neural Networks" has recently been awarded by the Best Paper at the Deep Learning on Graphs Workshop at KDD 2021.
We Provide Open Source Projects for Researchers and Developers
DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow.
DGL-LifeSci is a specialized package for applications in bioinformatics and cheminformatics powered by graph neural networks.
DGL-KE is an easy-to-use and highly scalable package for learning large-scale knowledge graph embeddings.
Amazon Web Services AI Shanghai Lablet is hiring full-time positions and intern positions. Please check the Amazon recruitment website for more information and job descriptions. Welcome to apply via the position link.