for Scalable and Reliable Machine LearningDMLC is a group to collaborate on open-source machine learning projects, with a goal of making cutting-edge large-scale machine learning widely available. The contributors includes researchers, PhD students and data scientists who are actively working on the field.
Flexible and Efficient Deep Learning Library on Heterogeneous Distributed Systems
General purpose gradient boosting library, including generalized linear model and gradient boosted decision trees
The NumPy interface upon MXNet’s backend
Data I/O for filesystems such as HDFS and Amazon S3, with job launchers for Yarn, MPI, ...
The parameter server framework for asynchronous key-value push and pull
A light weight library providing fault tolerant Allreduce and Broadcast
A lightweight CPU/GPU Matrix/Tensor Template Library.
We sincerely thank the following organizations (alphabetical order) for sponsoring the major developers of DMLC



and supporting the DMLC projects