Apache MXNet (incubating) for Deep Learning
Apache MXNet (incubating) 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, scaling effectively to multiple GPUs and multiple machines.
MXNet is also more than a deep learning project. It is also a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.
What's New
Contents
Features
Design notes providing useful insights that can re-used by other DL projects
Flexible configuration for arbitrary computation graph
Mix and match imperative and symbolic programming to maximize flexibility and efficiency
Lightweight, memory efficient and portable to smart devices
Scales up to multi GPUs and distributed setting with auto parallelism
Support for Python, R, Scala, C++ and Julia
Cloud-friendly and directly compatible with S3, HDFS, and Azure
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License
Licensed under an Apache-2.0 license.
Reference Paper
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
History
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.