Apache MXNet

Apache MXNet is a deep learning framework designed for both efficiency and flexibility . It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains 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. MXNet is portable and lightweight, scalable to many GPUs and machines. NumPy-like programming interface, and is integrated with the new, easy-to-use Gluon 2.0 interface. NumPy users can easily adopt MXNet and start in deep learning. Automatic hybridization provides imperative programming with the performance of traditional symbolic programming. Lightweight, memory-efficient, and portable to smart devices through native cross-compilation support on ARM, and through ecosystem projects such as TVM, TensorRT, OpenVINO. Scales up to multi GPUs and distributed setting with auto parallelism through ps-lite, Horovod, and BytePS. Extensible backend that supports full customization, allowing integration with custom accelerator libraries and in-house hardware without the need to maintain a fork. Support for Python, Java, C++, R, Scala, Clojure, Go, Javascript, Perl, and Julia. Cloud-friendly and directly compatible with AWS and Azure. oneDNN for Faster CPU Performance MXNet Memory Monger, Training Deeper Nets with Sublinear Memory Cost Tutorial for NVidia GTC 2016 MXNet.js: Javascript Package for Deep Learning in Browser (without server) Guide to Creating New Operators (Layers) Go binding for inference MXNet emerged from a collaboration by the authors of cxxnet, minerva, and purine2. The project reflects what we have learned from the past projects. MXNet combines aspects of each of these projects to achieve flexibility, speed, and memory efficiency. Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. In Neural Information Processing Systems, Workshop on Machine Learning Systems, 2015