Deep Graph Library
Recently, I am working on an open source project called Deep Graph Library (DGL) which is a python package that interfaces between existing tensor libraries and data being expressed as graphs.
The goal of this project is to make deep learning on graph structures easy and efficient. Popular applications of our Deep Graph Library include (but not limited to) Graph Convolutional Network and TreeLSTM models.
This is a joint project with NYU Shanghai and AWS AI Lab.
My previous project BatchMaker is an inference system for Recurrent Neural Network (RNN). The goal of BatchMaker is to provide low latency and high throughput when serving RNN models.
In this project, we proposed a new batching techniques called Cellular Batching, which exploits the recursive nature and variable-length property of Recurrent Neural Network to batch cells of different requests at different time step together. With Cellular Batching, BatchMaker allows incoming requests to join execution of current running batch, and allows short requests to return to user without being delayed by long requests.
When you outsource your application and host it on a cloud service, how can you be sure that the cloud faithfully executed your program? Orochi is an answer to this fundamental problem.
Orochi provides an abstract solution called SSCO which requires slight modification to outsourced application to log hints (which we don’t trust) during runtime, and later verifies the trace (request and response) and hints offline efficiently.