Open Source Projects
Our group develops open-source learning systems with the goal of transforming the industry and research landscapes. Here is a list of key open-source projects that we developed.
Apache TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
TVM stack is a unified optimization stack that will close the gap between the productivity-focused deep learning frameworks, and the performance- or efficiency-oriented hardware backends.
XGBoost: Scalable Tree Boosting
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. XGBoost was used to solve many machine learning challenges and has been deployed in production. You can use it in any of your favorite language including python, R, Julia, java, scala. The distributed version can be easily deployed on Hadoop, MPI, SGE and more recently DataFlow frameworks(e.g. Flink and Spark)
Apache MXNet: Efficient and Flexible Deep Learning
MXNet stands for mix and maximize. The idea is to combine the power of declartive programming together with imperative programming. In its core, a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. The library is portable and lightweight, and it scales to multiple GPUs and multiple machines.